CN109377329B - House resource recommendation method and device, storage medium and electronic equipment - Google Patents

House resource recommendation method and device, storage medium and electronic equipment Download PDF

Info

Publication number
CN109377329B
CN109377329B CN201811593699.1A CN201811593699A CN109377329B CN 109377329 B CN109377329 B CN 109377329B CN 201811593699 A CN201811593699 A CN 201811593699A CN 109377329 B CN109377329 B CN 109377329B
Authority
CN
China
Prior art keywords
house source
house
source
target
sources
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201811593699.1A
Other languages
Chinese (zh)
Other versions
CN109377329A (en
Inventor
朱超
陶海洋
廖智文
马文龙
王潇
洪定坤
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Youzhuju Network Technology Co Ltd
Original Assignee
Beijing Shiguang Renran Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Shiguang Renran Technology Co ltd filed Critical Beijing Shiguang Renran Technology Co ltd
Priority to CN201811593699.1A priority Critical patent/CN109377329B/en
Publication of CN109377329A publication Critical patent/CN109377329A/en
Application granted granted Critical
Publication of CN109377329B publication Critical patent/CN109377329B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/16—Real estate

Abstract

The embodiment of the disclosure discloses a house source recommending method and device, a storage medium and electronic equipment. The method comprises the following steps: screening hot house sources from a house source database based on a first preset condition, and determining strategy house sources from the house source database based on a second preset condition; wherein the first preset condition is different from the second preset condition; determining an initial recommended house source from a house source database according to the historical behavior data of the user; building a house source recommendation candidate set based on the popular house source, the strategy house source and the initial recommendation house source; and determining a target recommended house source from the house source recommendation candidate set according to the weight values distributed to the popular house source, the strategy house source and the initial recommended house source respectively, and recommending the target recommended house source to the user. By adopting the technical scheme of the embodiment of the disclosure, not only can appropriate house source information be recommended to the user, the problem of low house source searching efficiency is solved, but also the real requirements of the user on house sources can be accurately and quickly met, and the conversion rate of the user for searching the house sources is improved.

Description

House resource recommendation method and device, storage medium and electronic equipment
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to a room source recommending method and device, a storage medium and electronic equipment.
Background
At present, house property trading is more and more frequent in life, and with the development of internet technology, more and more second-hand house trading platforms based on the internet appear. In the second-hand house transaction process, a plurality of house purchasing users do not have specific house purchasing targets at the initial stage, and need to acquire house sources suitable for the needs of the users through recommendation of a transaction platform, so that time waste caused by massive search is avoided.
However, when recommending house resources, the house trading platform in the market recommends house resources mainly according to user positioning, or browses the records of house resources according to the history of the user, and recommends house resources for the user. The house source recommending method cannot push proper house source information to the user, and the real requirements of the user are difficult to meet.
Disclosure of Invention
The embodiment of the disclosure provides a house source recommending method and device, a storage medium and electronic equipment, which can recommend appropriate house source information to a user.
In a first aspect, an embodiment of the present disclosure provides a house source recommendation method, including:
screening hot house sources from a house source database based on a first preset condition, and determining strategy house sources from the house source database based on a second preset condition; wherein the first preset condition is different from the second preset condition;
determining an initial recommended house source from the house source database according to the historical behavior data of the user;
building a house source recommendation candidate set based on the popular house source, the strategy house source and the initial recommendation house source;
and determining a target recommended house source from the house source recommendation candidate set according to the weight values respectively allocated to the popular house source, the strategy house source and the initial recommended house source, and recommending the target recommended house source to the user.
In the foregoing scheme, optionally, before the hot house source is screened from the house source database based on the first preset condition, the method further includes:
acquiring a target house source matched with the city of the user in the house source database;
and screening out hot house sources from the house source database based on a first preset condition, wherein the hot house sources comprise at least one of the following items:
according to the region to which each target house source belongs, counting the number of first house sources contained in each region, determining the target region with the number of the first house sources larger than a first preset number threshold, and determining the house source corresponding to the target region as a hot house source;
according to the price of each target house source, counting the number of second house sources contained in each preset house source price interval, determining a target house source price interval with the number of the second house sources larger than a second preset number threshold value, and determining the house source corresponding to the target house source price interval as a popular house source;
and acquiring the historical click times of the user on each target house source in the house source database, and determining the house source with the historical click times larger than a preset time threshold value as the popular house source.
In the foregoing scheme, optionally, determining an initial recommended house source from the house source database according to the historical behavior data of the user includes:
setting corresponding house source characteristic weights for each house source characteristic related in the house source database according to the historical behavior data of the user;
determining a first target house source characteristic from each house source characteristic according to the house source characteristic weight; the house source characteristic weight corresponding to the first target house source characteristic is larger than a first preset weight threshold value;
and searching a first target room source matched with the first target room source characteristic from the room source database, and taking the first target room source as an initial recommended room source.
In the foregoing scheme, optionally, after searching for a first target room source matched with the first target room source feature from the room source database, and taking the first target room source as an initial recommended room source, the method includes:
acquiring latest historical behavior data of a user;
adjusting the step length according to a preset weight, and adjusting the house source characteristic weight corresponding to each house source characteristic based on the latest user historical behavior data;
determining a second target house source characteristic from each house source characteristic according to the adjusted house source characteristic weight; the house source characteristic weight corresponding to the second target house source characteristic is greater than a second preset weight threshold;
and searching a second target house source matched with the second target house source characteristic from the house source database to update the initial recommended house source.
In the foregoing solution, optionally, the house source characteristics include at least one of a city, a city district, a business district, a district to which the house source belongs, a house type, an area, a floor, an orientation, and a price.
In the foregoing scheme, optionally, recommending the target recommended house source to the user includes:
forming a house source recommendation list by the target recommended house source;
and recommending the house source recommendation list to a user.
In the foregoing scheme, optionally, popular house sources, strategic house sources and initial recommended house sources in the target recommended house sources are displayed at intervals in the house source recommendation list, wherein the continuous number of the target recommended house sources belonging to the popular house sources is smaller than a third preset number threshold, the continuous number of the target recommended house sources belonging to the strategic house sources is smaller than a fourth preset number threshold, and the continuous number of the target recommended house sources belonging to the initial recommended house sources is smaller than a fifth preset number threshold.
In a second aspect, an embodiment of the present disclosure further provides a room source recommending apparatus, where the apparatus includes:
the system comprises a hot house source and strategy house source determining module, a hot house source and strategy house source determining module and a strategy house source determining module, wherein the hot house source and strategy house source determining module is used for screening out a hot house source from a house source database based on a first preset condition and determining a strategy house source from the house source database based on a second preset condition; wherein the first preset condition is different from the second preset condition;
the initial recommended house source determining module is used for determining an initial recommended house source from the house source database according to the historical behavior data of the user;
the house source recommendation candidate set building module is used for building a house source recommendation candidate set based on the popular house source, the strategy house source and the initial recommendation house source;
and the target recommended house source determining module is used for determining a target recommended house source from the house source recommended candidate set according to the weight values respectively distributed to the popular house source, the strategic house source and the initial recommended house source, and recommending the target recommended house source to the user.
In the foregoing solution, optionally, the house source recommending apparatus further includes:
the target house source determining module is used for acquiring a target house source matched with the city of the user in the house source database before the hot house source is screened out from the house source database based on a first preset condition;
and screening out hot house sources from the house source database based on a first preset condition, wherein the hot house sources comprise at least one of the following items:
according to the region to which each target house source belongs, counting the number of first house sources contained in each region, determining the target region with the number of the first house sources larger than a first preset number threshold, and determining the house source corresponding to the target region as a hot house source;
according to the price of each target house source, counting the number of second house sources contained in each preset house source price interval, determining a target house source price interval with the number of the second house sources larger than a second preset number threshold value, and determining the house source corresponding to the target house source price interval as a popular house source;
and acquiring the historical click times of the user on each target house source in the house source database, and determining the house source with the historical click times larger than a preset time threshold value as the popular house source.
In the foregoing scheme, optionally, the initial recommended house source determining module is specifically configured to:
setting corresponding house source characteristic weights for each house source characteristic related in the house source database according to the historical behavior data of the user;
determining a first target house source characteristic from each house source characteristic according to the house source characteristic weight; the house source characteristic weight corresponding to the first target house source characteristic is larger than a first preset weight threshold value;
and searching a first target room source matched with the first target room source characteristic from the room source database, and taking the first target room source as an initial recommended room source.
In the foregoing solution, optionally, the house source recommending apparatus further includes:
the user historical behavior data acquisition module is used for searching a first target house source matched with the first target house source characteristic from the house source database, and acquiring the latest user historical behavior data after the first target house source is used as an initial recommended house source;
the house source characteristic weight adjusting module is used for adjusting step length according to preset weight and adjusting the house source characteristic weight corresponding to each house source characteristic based on the latest historical user behavior data;
the target house source characteristic determining module is used for determining second target house source characteristics from all house source characteristics according to the adjusted house source characteristic weight; the house source characteristic weight corresponding to the second target house source characteristic is greater than a second preset weight threshold;
and the initial recommended house source updating module is used for searching a second target house source matched with the second target house source characteristic from the house source database so as to update the initial recommended house source.
In the foregoing solution, optionally, the house source characteristics include at least one of a city, a city district, a business district, a district to which the house source belongs, a house type, an area, a floor, an orientation, and a price.
In the foregoing scheme, optionally, the target recommended house source recommending module is specifically configured to:
forming a house source recommendation list by the target recommended house source;
and recommending the house source recommendation list to a user.
In the foregoing scheme, optionally, popular house sources, strategic house sources and initial recommended house sources in the target recommended house sources are displayed at intervals in the house source recommendation list, wherein the continuous number of the target recommended house sources belonging to the popular house sources is smaller than a third preset number threshold, the continuous number of the target recommended house sources belonging to the strategic house sources is smaller than a fourth preset number threshold, and the continuous number of the target recommended house sources belonging to the initial recommended house sources is smaller than a fifth preset number threshold.
In a third aspect, embodiments of the present disclosure further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a house source recommendation method according to an embodiment of the present disclosure.
In a fourth aspect, an embodiment of the present application provides an electronic device, including:
one or more processors;
a memory for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a method of premises recommendation as described in embodiments herein.
The embodiment of the disclosure provides a house source recommendation scheme, which includes screening out a hot house source from a house source database based on a first preset condition, determining a strategy house source from the house source database based on a second preset condition, wherein the first preset condition is different from the second preset condition, determining an initial recommended house source from the house source database according to user historical behavior data, then constructing a house source recommendation candidate set based on the hot house source, the strategy house source and the initial recommended house source, and finally determining a target recommended house source from the house source recommendation candidate set according to weighted values respectively allocated to the hot house source, the strategy house source and the initial recommended house source, and recommending the target recommended house source to a user. By adopting the technical scheme of the embodiment of the disclosure, the house source recommended candidate set constructed by the popular house source, the strategic house source and the initial recommended house source screened from the house source database is used for determining the house source finally recommended to the user, so that not only can appropriate house source information be recommended to the user, the problem of low house source searching efficiency be solved, but also the real requirements of the user on the house source can be accurately and quickly met, and the conversion rate of the user for searching the house source is improved.
Drawings
Fig. 1 is a flowchart of a house source recommendation method according to an embodiment of the present invention;
FIG. 2 is a flow chart of another room source recommendation method provided by the embodiments of the present disclosure;
fig. 3 is a flowchart of another room source recommending method provided by the embodiment of the disclosure;
fig. 4 is a schematic structural diagram of a house source recommending apparatus according to an embodiment of the disclosure;
fig. 5 is a block diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Fig. 1 is a flowchart of a house source recommending method according to an embodiment of the present invention, which may be executed by a house source recommending apparatus, which may be composed of hardware and/or software and may be generally integrated in an electronic device. As shown in fig. 1, the method may include the steps of:
step 110, a hot house source is screened out from a house source database based on a first preset condition, and a strategy house source is determined from the house source database based on a second preset condition.
Wherein the first preset condition is different from the second preset condition.
It is understood that the house source database contains house source information in cities throughout the country. In this embodiment, a popular house source is screened out from a house source database based on a first preset condition, and a strategic house source is screened out from the house source database based on a second preset condition, wherein the popular house source can be understood as a house source in a popular city and a popular area at the current time or a house source concerned or favored by most users, and the strategic house source can be understood as a house source recommendation strategy formulated according to current service requirements and matched with the recommendation strategy. Illustratively, the house source recommendation strategy made according to the service requirement is a house source of a main push small house type (for example, one or two living rooms, or a house area smaller than a preset area threshold), and then the small house type house source screened from the house source database is used as the strategy house source. And if the house source recommendation strategy made according to the service demand is mainly pushing the B area in the A city and the house source in the c-d price interval, screening the B area in the A city from the house source database, and taking the house source in the c-d price interval as the strategy house source. It should be noted that, in this embodiment, the second preset condition for determining the policy house source from the house source database is not limited.
Optionally, before the hot house source is screened from the house source database based on the first preset condition, the method further includes: acquiring a target house source matched with the city of the user in the house source database; and screening out hot house sources from the house source database based on a first preset condition, wherein the hot house sources comprise at least one of the following items: according to the region to which each target house source belongs, counting the number of first house sources contained in each region, determining the target region with the number of the first house sources larger than a first preset number threshold, and determining the house source corresponding to the target region as a hot house source; according to the price of each target house source, counting the number of second house sources contained in each preset house source price interval, determining a target house source price interval with the number of the second house sources larger than a second preset number threshold value, and determining the house source corresponding to the target house source price interval as a popular house source; and acquiring the historical click times of the user on each target house source in the house source database, and determining the house source with the historical click times larger than a preset time threshold value as the popular house source.
For example, since the room source database includes a large number of room sources in cities across the country, and for a certain user, the user is more interested in the city where the user is located or the room source in the city where the user is located, a target room source matched with the city of the user is obtained from the room source database, where the city of the user may be a city where the user is located by searching for the APP of the room source (for example, the city where the user is located or the city where the user is located and is interested by the user), and then the popular room source is screened from the target room source in the room source database based on a first preset condition.
The specific method for determining the hot house source is not limited in this embodiment, and can be set according to actual requirements. Illustratively, the hot house source is screened out from the target house sources in the house source database based on a first preset condition, which includes at least one of the following:
(1) according to the area to which each target house source belongs, the number of the first house sources contained in each area is counted, the target area with the number of the first house sources larger than a first preset number threshold is determined, and the house source corresponding to the target area is determined as the hot house source.
Wherein, the region to which each target house source belongs comprises each city district or each business district. Illustratively, the region to which each target house source belongs includes A, E, F and four city jurisdictions G, and then the house source number of the target house source included in A, E, F and the four city jurisdictions G is counted respectively as the first house source number. And taking the target house sources contained in the municipal administration area with the number larger than the first preset number threshold value in the number of the first house sources as hot house sources. For example, if the number of the house sources of the target house sources included in the prefecture of E city is greater than a first preset number threshold, the target house sources included in the prefecture of E city are determined as popular house sources. The first preset number threshold may be set according to a user requirement, for example, an average value of the first house source number included in each area may be used as the first preset number threshold.
(2) And counting the number of second house sources contained in each preset house source price interval according to the price of each target house source, determining the target house source price interval with the number of the second house sources larger than a second preset number threshold value, and determining the house source corresponding to the target house source price interval as the hot house source.
And counting the room source quantity of the target room source contained in each preset room source price interval as the second room source quantity according to the price of each target room source. For example, the preset house source price intervals are a-b, b-c, c-d, and c-e, where a < b < d < e, the second house source quantities of the target house sources included in the four house source price intervals a-b, b-c, c-d, and c-e are respectively counted, and the target house sources included in the house source price intervals with the second house source quantity greater than the second preset quantity threshold value in the second house source quantities are used as hot house sources. For example, if the house source quantity of the target house source included in the house source price interval b-c is greater than the second preset quantity threshold, the target house source in the house source price interval b-c is determined as the popular house source. The second preset quantity threshold may be set according to a user requirement, for example, the average value of the second house source quantity included in each house source price interval may be used as the second preset quantity threshold.
It should be noted that, in this embodiment, the size relationship between the first preset number threshold and the second preset number threshold is not limited, where the first preset number threshold may be greater than the second preset number threshold, the first preset number threshold may be smaller than the second preset number threshold, and the first preset number threshold may also be the same as the second preset number threshold.
(3) And acquiring the historical click times of each target house source in the house source database by the user, and determining the house source with the historical click times larger than a preset time threshold as the popular house source.
When each user browses the house source in the house source database, the house source interested by the user can be clicked to view. Illustratively, historical click times of all users on each target house source in the house source database are obtained, and the house source with the historical click times larger than a preset time threshold is determined as the popular house source. It can be understood that the house source with the historical click times larger than the preset time threshold value is the house source which is concerned by most users, and therefore, the house source can be determined as the popular house source.
Optionally, when the user uses the house source search APP for the first time, the house source of the set data may be used as the hot house source based on the default ordering of the house source information in the house source database.
And step 120, determining an initial recommended house source from a house source database according to the historical behavior data of the user.
For example, the user historical behavior data may include at least one of time of stay on a page corresponding to the house source information, times of clicking to view the house source information, times of collecting the house source information, times of calling to inquire the house source information, and the like. Determining an initial recommended house source from a house source database according to the historical behavior data of the user may include: determining user historical behavior data corresponding to each house source in a house source database, and counting data quantity contained in the corresponding user historical behavior data, wherein if the user historical behavior data comprises the number of times of clicking to check house source information, the number of times of collecting the house source information and the number of times of calling to inquire the house source information, the number of times of clicking to check the house source information, the number of times of collecting the house source information and the number of times of calling to inquire the house source information are summed, and the operation result is used as the data quantity contained in the user historical behavior data. And determining the house source containing the data quantity in the user historical behavior data larger than a preset data quantity threshold value in the house source database as an initial recommended house source. Optionally, determining an initial recommended house source from a house source database according to the historical behavior data of the user may include: counting the times of inquiring the information of each house source by a user calling in the house source database, and determining the house source with the calling inquiry times larger than the preset inquiry times as an initial recommended house source. It should be noted that, in this embodiment, a specific manner for determining the initial recommended house source from the house source database according to the historical behavior data of the user is not limited.
And step 130, building a house source recommendation candidate set based on the popular house source, the strategy house source and the initial recommendation house source.
In this embodiment, the trending sources and strategic sources determined in step 110 and the initial recommended sources determined in step 120 are summarized to generate a candidate set of house source recommendations. The determined popular house source, the policy house source and the initial recommended house source may include one or more same house sources, that is, a certain house source belongs to any two of the popular house source, the policy house source and the initial recommended house source, or a certain house source belongs to the popular house source, the policy house source and the initial recommended house source. Therefore, the hot house source, the strategy house source and the initial recommended house source can be subjected to de-duplication processing and then summarized to generate the house source recommendation candidate set.
And step 140, determining a target recommended house source from the house source recommendation candidate set according to the weight values respectively allocated to the popular house source, the strategy house source and the initial recommended house source, and recommending the target recommended house source to the user.
For example, corresponding weight values may be respectively allocated to the popular house source, the strategic house source and the initial recommended house source in advance, where the weight values allocated to the three house sources may be the same or different, and the size of the weight values of the three house sources is not limited in this embodiment. For example, in the trending house source, the policy house source and the initial recommended house source, the user pays more attention to the current most trending house source, and then pays more attention to the policy house source pushed according to business requirements, and when the initial recommended house source is not paid much attention, the weight value of the trending house source may be set to 0.5, the weight value of the policy house source may be set to 0.3, and the weight value of the initial recommended house source may be set to 0.2.
In this embodiment, 100 sets of popular house sources and 80 sets of policy house sources are screened out from the house source database through step 110, 50 sets of initial recommended house sources are determined from the house source database through step 120, and if there are no duplicate identical house sources in the popular house sources, the policy house sources and the initial recommended house sources, the room source recommended candidate set constructed based on the three types of house sources contains 230 sets of house sources in common. For example, if the weights assigned to the hot house source, the strategic house source and the initial recommended house source are set to 0.5, 0.3 and 0.2, respectively, the target recommended house sources determined from the house source recommendation candidate set include 84(84 ═ 100 × 0.5+80 × 0.3+50 × 0.2) sets, where the 84 sets of target recommended house sources include 50 sets of hot house sources, 24 sets of strategic house sources and 10 sets of initial recommended house sources. Then, the 84 sets of target recommended house sources are recommended to the user, for example, the 84 sets of target house sources are displayed in a list form in a house source information query APP of the user using the mobile terminal.
Optionally, the popular house sources screened from the house source database in step 110 are sorted according to the popular degree of the house sources, for example, the house sources with more historical click times of the user are more popular, or the house sources with lower price are more popular, or the house sources with more luxurious area, more convenient traffic in the area where the house sources belong, and more popular house sources. Similarly, the strategic house sources screened from the house source database through step 110 may also be sorted in a certain order, and if the small house type house sources are used as the strategic house sources, the house sources may be sorted in an order from small to large in area. For the initial recommended house sources screened from the house source database through step 120, the house sources may be sorted in the order of the number of the user historical behavior data. Then, when the target recommended house source is determined from the house source recommendation candidate set according to the weight values respectively allocated to the popular house source, the strategic house source and the initial recommended house source, the house source with the top ranking among the popular house source, the strategic house source and the initial recommended house source can be used as the target recommended house source.
It should be noted that, the execution sequence of step 110 and step 120 is not limited in this embodiment, and step 110 may be executed first, and then step 120 may be executed; step 120 may be performed first, step 110 may be performed later, and step 110 and step 120 may be performed simultaneously.
The house source recommending method provided by the embodiment of the disclosure screens out the hot house source from the house source database based on the first preset condition, and determines the strategy house source from the house source database based on the second preset condition, wherein the first preset condition is different from the second preset condition, and determines the initial recommended house source from the house source database according to the user historical behavior data, then constructs a house source recommending candidate set based on the hot house source, the strategy house source and the initial recommended house source, and finally determines the target recommended house source from the house source recommending candidate set according to the weighted values respectively allocated to the hot house source, the strategy house source and the initial recommended house source, and recommends the target recommended house source to the user. By adopting the technical scheme of the embodiment of the disclosure, the house source finally recommended to the user is determined by recommending the candidate set of the house source constructed by the popular house source, the strategic house source and the initially recommended house source in the house source database, so that not only can appropriate house source information be recommended to the user, the problem of low house source searching efficiency be solved, but also the real demand of the user on the house source can be accurately and quickly met, and the conversion rate of the user for searching the house source is improved.
Fig. 2 is a flowchart of another room source recommendation method provided in the embodiment of the present disclosure. The present embodiment is specifically optimized based on various alternatives in the above-described embodiments. As shown in fig. 2, the method comprises the steps of:
step 210, a hot house source is screened out from a house source database based on a first preset condition, and a strategy house source is determined from the house source database based on a second preset condition.
Wherein the first preset condition is different from the second preset condition.
And step 220, setting corresponding house source characteristic weights for the house source characteristics related to the house source database according to the historical behavior data of the user.
Optionally, the house source characteristics include at least one of a city, a city district, a business district, a district to which the house source belongs, a house type, an area, a floor, an orientation, and a price. In this embodiment, a corresponding house source feature weight is set for each house source feature related in the house source database according to the user historical behavior data, where the house source feature weight allocated to the house source feature is larger for the house source feature that includes more user historical behavior data, or the house source feature weight allocated to the house source feature is larger for the house source feature that is determined according to the user historical behavior data and is more interested by the user. If the number of times of clicking to view the house source information or the number of times of calling to inquire the house source information in the historical behavior data of the user is used as a standard for measuring the degree of interest of the user in each house source characteristic, the more the number of times of clicking to view the house source information is, the more the user is interested in the house source characteristic contained in the corresponding house source information, and the more the number of times of calling to inquire the house source information is, the more the user is interested in the house source characteristic contained in the corresponding house source information.
Illustratively, the house source characteristics involved in the house source database comprise three characteristics of a city district, an orientation and a price, wherein the city district involves A, B, C and D, the orientation involves four orientations of east, south, west and north, and the price involves three price intervals of a-b, b-c and c-D. Taking the house source feature of the urban district as an example, the historical behavior data of the users associated with the house sources related to A, B, C and D four areas are respectively counted, for example, the number of telephone dialing queries related to the house sources related to A, B, C and D four areas is counted, that is, the number of telephone dialing queries of the users to the house sources of A, B, C and D four areas is counted, wherein the greater the number of telephone dialing queries is, the greater the weight setting of the house source feature of the corresponding urban district is. For example, if the number of times of the statistical user's telephone calls inquiring the house resources in A, B, C and D four areas is 1000, 300, 500, or 200 times, the house resource feature weight in the district of city A may be 0.5, the house resource feature weight in the district of city B may be 0.15, the house resource feature weight in the district of city C may be 0.25, and the house resource feature weight in the district of city D may be 0.1. Similarly, for the house source feature, the historical behavior data of the users related to the house sources related to the east, south, west and north are respectively counted, and the corresponding house source feature weight is set for each orientation according to the historical behavior data of the users related to each orientation. And for the house source feature of the price, respectively counting the user historical behavior data associated with the house sources related to the three price intervals a-b, b-c and c-d, and setting corresponding house source feature weights for the price intervals according to the user historical behavior data associated with the price intervals.
And step 230, determining a first target house source characteristic from all house source characteristics according to the house source characteristic weight.
And the house source characteristic weight corresponding to the first target house source characteristic is greater than a first preset weight threshold value.
In this embodiment, a first target house source characteristic is determined from the house source characteristics according to the house source characteristic weight set in step 220, for example, a house source characteristic with a house source characteristic weight greater than a first preset weight threshold is determined as a target house source characteristic. Illustratively, for the house source feature in the prefecture, the house source feature weights of A, B, C and D are 0.5, 0.15,0.25 and 0.1 respectively; for the orientation of the house source feature, the weights of the house source features in the east, south, west and north orientations are 0.3,0.4,0.2 and 0.1 respectively; for the price, namely the property feature, the weights of the property features of the three price intervals of a-b, b-c and c-d are respectively 0.6,0.35 and 0.15. If the first preset weight threshold is 0.3, the prefecture, south orientation and a-b, b-c (namely a-c) price interval of the city A are used as the first target house source characteristics.
Step 240, searching a first target room source matched with the first target room source characteristic from the room source database, and taking the first target room source as an initial recommended room source.
Illustratively, first target house sources matched with the first target house source characteristics of the prefecture, south orientation and a-b, b-c (namely a-c) price interval of the city A are searched from the house source database to serve as initial recommended house sources. Namely, the house source in the district of the city A, with the south orientation and the price in the price range of a-c is screened out from the house source database as the initial recommended house source.
And step 250, building a house source recommendation candidate set based on the popular house source, the strategy house source and the initial recommendation house source.
And step 260, determining a target recommended house source from the house source recommendation candidate set according to the weight values respectively allocated to the popular house source, the strategy house source and the initial recommended house source, and recommending the target recommended house source to the user.
It should be noted that, the execution sequence of the steps 210 and 220-240 is not limited in this embodiment, and the step 210 may be executed first, and then the step 220-240 may be executed; step 220 and 240 may be performed first, then step 210 may be performed, and step 210 and step 220 and 240 may be performed simultaneously.
In the technical scheme of the embodiment, corresponding house source characteristic weights are set for each house source characteristic related in the house source database according to the historical behavior data of the user, a first target house source characteristic is determined from each house source characteristic according to the house source characteristic weights, wherein, the house source characteristic weight corresponding to the first target house source characteristic is larger than a first preset weight threshold, and finally, a first target house source matched with the first target house source characteristic is searched from the house source database, and the first target house source is used as an initial recommended house source, the target house source characteristics in various house source characteristics can be determined according to the historical behavior data of the user, and the matched house source is screened out from the house source database according to the characteristics of the target house source and is used as an initial recommended house source, the matching degree of the determined initial recommended house source and the historical behavior data of the user can be improved, so that the screened initial recommended house source can better meet the requirements of the user.
In some embodiments, after searching the room source database for a first target room source matching with the first target room source feature and using the first target room source as an initial recommended room source, the method includes: acquiring latest historical behavior data of a user; adjusting the step length according to a preset weight, and adjusting the house source characteristic weight corresponding to each house source characteristic based on the latest user historical behavior data; determining a second target house source characteristic from each house source characteristic according to the adjusted house source characteristic weight; the house source characteristic weight corresponding to the second target house source characteristic is greater than a second preset weight threshold; and searching a second target house source matched with the second target house source characteristic from the house source database to update the initial recommended house source. The method has the advantages that the latest user historical behavior data can be obtained every other preset time, the house source characteristic weight set for each house source characteristic is readjusted according to the latest user historical behavior data, and accordingly the initial recommended house source is further updated, so that the initial recommended house source determined from the house source database is more matched with the user behavior data, and user requirements can be better met.
For example, as the user continuously browses the house source in the house source database over time, the historical behavior data of the user related to the house source may be continuously updated, and in order to ensure the accuracy and timeliness of the initially recommended house source determined from the house source database according to the historical behavior data of the user, the latest historical behavior data of the user is obtained. The latest historical behavior data of the user may include not only the historical behavior data of the user associated with the first target room source determined in step 240, but also the historical behavior data of the user associated with other room sources in the room source database except the first target room source. And adjusting the step length according to the preset weight, and adjusting the house source weight corresponding to each house source characteristic based on the latest historical behavior data of the user. Illustratively, taking the house source feature of the prefecture as an example, the latest user historical behavior data associated with house sources related to A, B, C and D four areas are counted respectively, such as the latest number of calls made to ask associated with house sources related to A, B, C and D four areas. For example, the counted number of latest call inquiries made by the user to the house resources of A, B, C and D regions is 1000, 500, 800 and 700 times, respectively, and if the preset weight adjustment step size is 0.06, the house resource feature weights of A, B, C and D regions can be adjusted from the original 0.5, 0.15,0.25 and 0.1 to 0.44,0.15,0.25 and 0.16. The weight adjustment step length set for each room source feature may be the same or different.
Illustratively, for the house source feature in the urban district, the weights of the house source feature adjusted in A, B, C and D are 0.44,0.15,0.25 and 0.16; for the orientation of the house source feature, the weights of the house source feature after the east, south, west and north orientations are adjusted to be 0.4,0.25,0.2 and 0.15 respectively; for the price, namely the property feature, the weights of the property features of the three price intervals of a-b, b-c and c-d are respectively 0.3,0.2 and 0.5. And if the first preset weight threshold is 0.24, taking the prefecture of the city A or the city C, the east orientation or the south orientation and the price interval of a-b, C-d as the second target house source characteristics. And then searching a second target room source matched with the characteristics of the second target room sources in the prefecture A or the prefecture C, the east direction or the south direction and the price intervals a-b, C-d from the room source database to be used as an initial recommended room source. Namely, the house source located in the district of the city A or the district of the city C, oriented to the east or south and having the price in the price range of a-b or C-d is screened out from the house source database to be used as a second target house source, and the initial recommended house source is updated based on the second target house source.
It should be noted that, in this embodiment, the magnitude relationship between the first preset weight threshold and the second preset weight threshold is not limited, where the first preset weight threshold may be greater than the second preset weight threshold, may also be smaller than the second preset weight threshold, and may also be equal to the second preset weight threshold.
Fig. 3 is a flowchart of another room source recommending method according to an embodiment of the disclosure. The present embodiment is specifically optimized based on various alternatives in the above-described embodiments. As shown in fig. 3, the method comprises the steps of:
step 310, a hot house source is screened out from a house source database based on a first preset condition, and a strategy house source is determined from the house source database based on a second preset condition.
Wherein the first preset condition is different from the second preset condition;
and 320, setting corresponding house source characteristic weights for the house source characteristics related to the house source database according to the historical behavior data of the user.
And step 330, determining a first target house source characteristic from each house source characteristic according to the house source characteristic weight.
And the house source characteristic weight corresponding to the first target house source characteristic is greater than a first preset weight threshold value.
Step 340, searching a first target room source matched with the first target room source characteristic from the room source database, and taking the first target room source as an initial recommended room source.
Step 350, building a house source recommendation candidate set based on the popular house source, the strategy house source and the initial recommendation house source;
and step 360, determining target recommended house sources from the house source recommendation candidate set according to the weight values respectively distributed to the popular house source, the strategy house source and the initial recommended house source.
And step 370, forming a room source recommendation list by the target recommended room sources.
Illustratively, the target recommended house sources determined from the house source recommendation candidate set in step 360 include three types of house sources, namely popular house sources, strategic house sources and initial recommended house sources, and the determined target recommended house sources are arranged one by one in a list form to form a house source recommendation list. In the process of forming the house source recommendation list by the target recommendation house sources, all the target recommendation house sources can be randomly arranged, and can also be arranged according to any arrangement and combination sequence mode of the categories of the three types of house sources (the sequence of the popular house sources, the strategy house sources and the initial recommendation house sources, the sequence of the popular house sources, the initial recommendation house sources and the strategy house sources, the sequence of the strategy house sources, the popular house sources and the initial recommendation house sources, the sequence of the strategy house sources, the initial recommendation house sources and the popular house sources, the sequence of the initial recommendation house sources, the strategy house sources and the popular house sources and the sequence of the initial recommendation house sources, the popular house sources and the strategy house sources).
Optionally, popular house sources, strategic house sources and initial recommended house sources in the target recommended house sources are displayed in the house source recommendation list at intervals, wherein the continuous number of the target recommended house sources belonging to the popular house sources is smaller than a third preset number threshold, the continuous number of the target recommended house sources belonging to the strategic house sources is smaller than a fourth preset number threshold, and the continuous number of the target recommended house sources belonging to the initial recommended house sources is smaller than a fifth preset number threshold. The method has the advantages that the user can be effectively prevented from browsing or checking the target recommended house sources of the same category for a long time, the user is prevented from browsing or checking the unicity of the target recommended house sources, the conversion rate of the user for searching the house sources can be effectively improved, and the real requirements of the user on the house sources are met.
Illustratively, popular house resources, strategy house resources and initial recommended house resources in the target recommended house resources are displayed at intervals in the house resource recommendation list, so that the phenomenon that the same type of target recommended house resources are displayed in a centralized mode is avoided. In addition, hot house sources, strategy house sources and initial recommended house sources in the target recommended house sources are displayed in an interspersed mode, the continuous number of the target recommended house sources belonging to the hot house sources is smaller than a third preset number threshold, the continuous number of the target recommended house sources belonging to the strategy house sources is smaller than a fourth preset number threshold, and the continuous number of the target recommended house sources belonging to the initial recommended house sources is smaller than a fifth preset number threshold. In this embodiment, the third preset number threshold, the fourth preset number threshold and the fifth preset number threshold are not limited in size, and the third preset number threshold, the fourth preset number threshold and the fifth preset number threshold may be partially the same, may also be completely the same, and may also be completely different.
And step 380, recommending the house source recommendation list to the user.
According to the technical scheme of the embodiment, a popular house source, a strategy house source and an initial recommended house source are screened from a house source database, a source recommendation candidate set is established based on the house sources, then a target recommended house source is determined from the house source recommendation candidate set according to weight values respectively distributed to the popular house source, the strategy house source and the initial recommended house source, the target recommended house source is formed into a house source recommendation list, and finally the house source recommendation list is recommended to a user. The method and the system can recommend proper house source information to the user, so that the user can know the relevant house source information clearly, the problem of low house source searching efficiency is solved, the real requirements of the user on house sources can be met accurately and quickly, and the conversion rate of the user for searching the house sources is improved.
Fig. 4 is a schematic structural diagram of a house source recommending apparatus according to an embodiment of the present disclosure, which is applicable to a case of recommending a house source to a user. The apparatus may be implemented in software and/or hardware, and may be configured in an electronic device. As shown in fig. 4, the apparatus may include:
a hot house source and policy house source determining module 410, configured to screen a hot house source from a house source database based on a first preset condition, and determine a policy house source from the house source database based on a second preset condition; wherein the first preset condition is different from the second preset condition;
an initial recommended house source determining module 420, configured to determine an initial recommended house source from the house source database according to the user historical behavior data;
the house source recommendation candidate set constructing module 430 is configured to construct a house source recommendation candidate set based on the popular house source, the strategic house source and the initial recommended house source;
and the target recommended house source recommending module 440 is configured to determine a target recommended house source from the house source recommendation candidate set according to the weight values respectively allocated to the popular house source, the strategic house source and the initial recommended house source, and recommend the target recommended house source to the user.
According to the technical scheme of the embodiment, a popular house source is screened out from a house source database based on a first preset condition, a strategy house source is determined from the house source database based on a second preset condition, wherein the first preset condition is different from the second preset condition, an initial recommended house source is determined from the house source database according to historical behavior data of a user, a house source recommendation candidate set is constructed based on the popular house source, the strategy house source and the initial recommended house source, a target recommended house source is determined from the house source recommendation candidate set according to weight values respectively distributed to the popular house source, the strategy house source and the initial recommended house source, and the target recommended house source is recommended to the user. By adopting the technical scheme of the embodiment of the disclosure, the house source finally recommended to the user is determined by recommending the candidate set of the house source constructed by the popular house source, the strategic house source and the initially recommended house source in the house source database, so that not only can appropriate house source information be recommended to the user, the problem of low house source searching efficiency be solved, but also the real requirements of the user can be accurately and quickly met, and the conversion rate of the user for searching the house source is improved.
Optionally, the house source recommending apparatus further includes:
the target house source determining module is used for acquiring a target house source matched with the city of the user in the house source database before the hot house source is screened out from the house source database based on a first preset condition;
and screening out hot house sources from the house source database based on a first preset condition, wherein the hot house sources comprise at least one of the following items:
according to the region to which each target house source belongs, counting the number of first house sources contained in each region, determining the target region with the number of the first house sources larger than a first preset number threshold, and determining the house source corresponding to the target region as a hot house source;
according to the price of each target house source, counting the number of second house sources contained in each preset house source price interval, determining a target house source price interval with the number of the second house sources larger than a second preset number threshold value, and determining the house source corresponding to the target house source price interval as a popular house source;
and acquiring the historical click times of the user on each target house source in the house source database, and determining the house source with the historical click times larger than a preset time threshold value as the popular house source.
Optionally, the initial recommended house source determining module is specifically configured to:
setting corresponding house source characteristic weights for each house source characteristic related in the house source database according to the historical behavior data of the user;
determining a first target house source characteristic from each house source characteristic according to the house source characteristic weight; the house source characteristic weight corresponding to the first target house source characteristic is larger than a first preset weight threshold value;
and searching a first target room source matched with the first target room source characteristic from the room source database, and taking the first target room source as an initial recommended room source.
Optionally, the house source recommending apparatus further includes:
the user historical behavior data acquisition module is used for searching a first target house source matched with the first target house source characteristic from the house source database, and acquiring the latest user historical behavior data after the first target house source is used as an initial recommended house source;
the house source characteristic weight adjusting module is used for adjusting step length according to preset weight and adjusting the house source characteristic weight corresponding to each house source characteristic based on the latest historical user behavior data;
the target house source characteristic determining module is used for determining second target house source characteristics from all house source characteristics according to the adjusted house source characteristic weight; the house source characteristic weight corresponding to the second target house source characteristic is greater than a second preset weight threshold;
and the initial recommended house source updating module is used for searching a second target house source matched with the second target house source characteristic from the house source database so as to update the initial recommended house source.
Optionally, the house source characteristics include at least one of a city, a city district, a business district, a district to which the house source belongs, a house type, an area, a floor, an orientation, and a price.
Optionally, the target recommended house source recommending module is specifically configured to:
forming a house source recommendation list by the target recommended house source;
and recommending the house source recommendation list to a user.
Optionally, popular house sources, strategic house sources and initial recommended house sources in the target recommended house sources are displayed in the house source recommendation list at intervals, wherein the continuous number of the target recommended house sources belonging to the popular house sources is smaller than a third preset number threshold, the continuous number of the target recommended house sources belonging to the strategic house sources is smaller than a fourth preset number threshold, and the continuous number of the target recommended house sources belonging to the initial recommended house sources is smaller than a fifth preset number threshold.
The embodiment of the disclosure also provides an electronic device, and the house source recommending device provided by the embodiment of the disclosure can be integrated in the electronic device. The electronic device of the disclosed embodiment includes a terminal device or a server, wherein the terminal device may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a fixed terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
Fig. 5 is a block diagram of an electronic device according to an embodiment of the present disclosure. The electronic device may include: one or more processors;
a memory for storing one or more programs,
when executed by the one or more programs, cause the one or more processors to implement a method comprising:
screening hot house sources from a house source database based on a first preset condition, and determining strategy house sources from the house source database based on a second preset condition; wherein the first preset condition is different from the second preset condition;
determining an initial recommended house source from the house source database according to the historical behavior data of the user;
building a house source recommendation candidate set based on the popular house source, the strategy house source and the initial recommendation house source;
and determining a target recommended house source from the house source recommendation candidate set according to the weight values respectively allocated to the popular house source, the strategy house source and the initial recommended house source, and recommending the target recommended house source to the user. It should be understood that the illustrated electronic device 500 is merely an example, and that the electronic device 500 may have more or fewer components than shown in the figures, may combine two or more components, or may have a different configuration of components. The various components shown in the figures may be implemented in hardware, software, or a combination of hardware and software, including one or more signal processing and/or application specific integrated circuits.
The following describes the electronic device integrated with the house source recommending apparatus provided in this embodiment in detail.
As shown in fig. 5, electronic device 500 may include a processor (e.g., central processing unit, graphics processor, etc.) 520 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)530 or a program loaded from memory 510 into a Random Access Memory (RAM) 540. In the RAM540, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processor 520, the ROM530, and the RAM540 are connected to each other through a bus 550. An input/output (I/O) interface 560 is also connected to bus 550.
Generally, the following devices may be connected to the I/O interface 560: input devices 580 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, or the like; output devices 590 including, for example, a Liquid Crystal Display (LCD), speaker, vibrator, etc.; a memory 510 including, for example, a tape, a hard disk, etc.; the electronic device 500 may also include a communications apparatus 570. The communication device 570 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program containing program code for performing a house source recommendation method provided by embodiments of the present disclosure. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means, or installed from the memory, or installed from the ROM. The computer program, when executed by a processor, performs the above-described functions defined in the house source recommendation method of the embodiments of the present disclosure.
It should be noted that the computer readable medium in the embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the disclosed embodiments, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to perform the method of:
screening hot house sources from a house source database based on a first preset condition, and determining strategy house sources from the house source database based on a second preset condition; wherein the first preset condition is different from the second preset condition;
determining an initial recommended house source from the house source database according to the historical behavior data of the user;
building a house source recommendation candidate set based on the popular house source, the strategy house source and the initial recommendation house source;
and determining a target recommended house source from the house source recommendation candidate set according to the weight values respectively allocated to the popular house source, the strategy house source and the initial recommended house source, and recommending the target recommended house source to the user.
Of course, the storage medium provided by the embodiments of the present disclosure contains computer-executable instructions, and the computer-executable instructions are not limited to the above-mentioned house source recommending operation, and may also perform related operations in the house source recommending method provided by any embodiments of the present disclosure.
The house resource recommending device, the storage medium and the electronic device provided in the above embodiments can execute the house resource recommending method provided in any embodiment of the disclosure, and have corresponding functional modules and beneficial effects for executing the method. Technical details that are not described in detail in the above embodiments may be referred to a house source recommendation method provided in any embodiment of the present disclosure.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules and units described in the embodiments of the present disclosure may be implemented by software or hardware. The names of the modules and units do not limit the modules or units in some cases.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (7)

1. A house source recommendation method, comprising:
screening hot house sources from a house source database based on a first preset condition, and determining strategy house sources from the house source database based on a second preset condition; wherein the first preset condition is different from the second preset condition; the strategy house source is matched with a house source recommendation strategy formulated according to the current service requirement;
determining an initial recommended house source from the house source database according to the historical behavior data of the user;
building a house source recommendation candidate set based on the popular house source, the strategy house source and the initial recommendation house source;
according to weight values distributed to the popular house source, the strategy house source and the initial recommended house source, a target recommended house source is determined from the house source recommendation candidate set, the target recommended house source is formed into a house source recommendation list, and the house source recommendation list is recommended to a user; the hot house sources, the strategy house sources and the initial recommended house sources in the target recommended house sources are displayed in the house source recommendation list at intervals, wherein the continuous number of the target recommended house sources belonging to the hot house sources is smaller than a third preset number threshold, the continuous number of the target recommended house sources belonging to the strategy house sources is smaller than a fourth preset number threshold, and the continuous number of the target recommended house sources belonging to the initial recommended house sources is smaller than a fifth preset number threshold;
determining an initial recommended house source from the house source database according to the historical behavior data of the user, wherein the method comprises the following steps:
setting corresponding house source characteristic weights for each house source characteristic related in the house source database according to the historical behavior data of the user; the more house source characteristics containing the historical behavior data of the user, the more house source characteristic weight distributed to the house source characteristics;
determining a first target house source characteristic from each house source characteristic according to the house source characteristic weight; the house source characteristic weight corresponding to the first target house source characteristic is larger than a first preset weight threshold value;
and searching a first target room source matched with the first target room source characteristic from the room source database, and taking the first target room source as an initial recommended room source.
2. The method of claim 1, further comprising, prior to screening the hot house source from the house source database based on the first predetermined condition:
acquiring a target house source matched with the city of the user in the house source database;
and screening out hot house sources from the house source database based on a first preset condition, wherein the hot house sources comprise at least one of the following items:
according to the region to which each target house source belongs, counting the number of first house sources contained in each region, determining the target region with the number of the first house sources larger than a first preset number threshold, and determining the house source corresponding to the target region as a hot house source;
according to the price of each target house source, counting the number of second house sources contained in each preset house source price interval, determining a target house source price interval with the number of the second house sources larger than a second preset number threshold value, and determining the house source corresponding to the target house source price interval as a popular house source;
and acquiring the historical click times of the user on each target house source in the house source database, and determining the house source with the historical click times larger than a preset time threshold value as the popular house source.
3. The method of claim 1, wherein after searching the room source database for a first target room source matching the first target room source characteristic and using the first target room source as an initial recommended room source, the method comprises:
acquiring latest historical behavior data of a user;
adjusting the step length according to a preset weight, and adjusting the house source characteristic weight corresponding to each house source characteristic based on the latest user historical behavior data;
determining a second target house source characteristic from each house source characteristic according to the adjusted house source characteristic weight; the house source characteristic weight corresponding to the second target house source characteristic is greater than a second preset weight threshold;
and searching a second target house source matched with the second target house source characteristic from the house source database to update the initial recommended house source.
4. The method of claim 1 or 3, wherein the house source characteristics comprise at least one of city, district of city, business district, cell to which house source belongs, house type, area, floor, orientation and price.
5. A house source recommendation device, comprising:
the system comprises a hot house source and strategy house source determining module, a hot house source and strategy house source determining module and a strategy house source determining module, wherein the hot house source and strategy house source determining module is used for screening out a hot house source from a house source database based on a first preset condition and determining a strategy house source from the house source database based on a second preset condition; wherein the first preset condition is different from the second preset condition; the strategy house source is matched with a house source recommendation strategy formulated according to the current service requirement;
the initial recommended house source determining module is used for determining an initial recommended house source from the house source database according to the historical behavior data of the user;
the house source recommendation candidate set building module is used for building a house source recommendation candidate set based on the popular house source, the strategy house source and the initial recommendation house source;
the target recommended house source recommending module is used for determining target recommended house sources from the house source recommending candidate set according to weight values distributed to the popular house source, the strategic house source and the initial recommended house source respectively, forming a house source recommending list by the target recommended house sources and recommending the house source recommending list to a user; the hot house sources, the strategy house sources and the initial recommended house sources in the target recommended house sources are displayed in the house source recommendation list at intervals, wherein the continuous number of the target recommended house sources belonging to the hot house sources is smaller than a third preset number threshold, the continuous number of the target recommended house sources belonging to the strategy house sources is smaller than a fourth preset number threshold, and the continuous number of the target recommended house sources belonging to the initial recommended house sources is smaller than a fifth preset number threshold;
wherein the initial recommended house source determining module is configured to:
setting corresponding house source characteristic weights for each house source characteristic related in the house source database according to the historical behavior data of the user; the more house source characteristics containing the historical behavior data of the user, the more house source characteristic weight distributed to the house source characteristics;
determining a first target house source characteristic from each house source characteristic according to the house source characteristic weight; the house source characteristic weight corresponding to the first target house source characteristic is larger than a first preset weight threshold value;
and searching a first target room source matched with the first target room source characteristic from the room source database, and taking the first target room source as an initial recommended room source.
6. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of room source recommendation according to any one of claims 1-4.
7. An electronic device, characterized in that the electronic device comprises:
one or more processors;
a memory for storing one or more programs,
when executed by the one or more programs, cause the one or more processors to implement the method of any of claims 1-4.
CN201811593699.1A 2018-12-25 2018-12-25 House resource recommendation method and device, storage medium and electronic equipment Active CN109377329B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201811593699.1A CN109377329B (en) 2018-12-25 2018-12-25 House resource recommendation method and device, storage medium and electronic equipment

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201811593699.1A CN109377329B (en) 2018-12-25 2018-12-25 House resource recommendation method and device, storage medium and electronic equipment

Publications (2)

Publication Number Publication Date
CN109377329A CN109377329A (en) 2019-02-22
CN109377329B true CN109377329B (en) 2022-03-04

Family

ID=65371514

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201811593699.1A Active CN109377329B (en) 2018-12-25 2018-12-25 House resource recommendation method and device, storage medium and electronic equipment

Country Status (1)

Country Link
CN (1) CN109377329B (en)

Families Citing this family (21)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110175190A (en) * 2019-04-15 2019-08-27 平安科技（深圳）有限公司 Source of houses recommended method, device, computer equipment and computer readable storage medium
CN110163685A (en) * 2019-05-27 2019-08-23 珠海幸福家网络科技股份有限公司 The customer priorities analysis system and analysis method of room track are seen based on client
CN110619027B (en) * 2019-06-18 2022-10-21 北京无限光场科技有限公司 House source information recommendation method and device, terminal equipment and medium
CN110598109A (en) * 2019-09-16 2019-12-20 上海喜马拉雅科技有限公司 Information recommendation method, device, equipment and storage medium
CN111080407A (en) * 2019-12-04 2020-04-28 青梧桐有限责任公司 House information recommendation method and device, electronic equipment and readable storage medium
CN110968801A (en) * 2019-12-04 2020-04-07 青梧桐有限责任公司 Real estate product searching method, storage medium and electronic device
CN112948721A (en) * 2019-12-09 2021-06-11 深圳市明源云客电子商务有限公司 Online opening method, device and equipment
CN111143689A (en) * 2019-12-31 2020-05-12 青梧桐有限责任公司 Method for constructing recommendation engine according to user requirements and user portrait
CN111161034A (en) * 2019-12-31 2020-05-15 青梧桐有限责任公司 Method for constructing recommendation engine based on LBS (location based service) renting scene
CN111159561A (en) * 2019-12-31 2020-05-15 青梧桐有限责任公司 Method for constructing recommendation engine according to user behaviors and user portrait
CN111159559A (en) * 2019-12-31 2020-05-15 青梧桐有限责任公司 Method for constructing recommendation engine according to user requirements and user behaviors
CN111311305A (en) * 2020-01-17 2020-06-19 青梧桐有限责任公司 Method and system for analyzing user public traffic band based on user track
CN111222960A (en) * 2020-01-17 2020-06-02 青梧桐有限责任公司 Room source recommendation method and system based on public traffic zone
CN111311363A (en) * 2020-02-12 2020-06-19 北京城市网邻信息技术有限公司 House source information recommendation method and device
CN111768274A (en) * 2020-06-24 2020-10-13 中国地质大学（武汉） Data classification storage system based on artificial intelligence
CN111652698B (en) * 2020-07-17 2020-12-29 江苏荣泽信息科技股份有限公司 Intelligent house renting system based on block chain
CN112037034A (en) * 2020-09-02 2020-12-04 中国银行股份有限公司 Financial product recommendation method and device, storage medium and electronic equipment
CN112464085B (en) * 2020-11-19 2023-04-07 贝壳技术有限公司 House source recommendation method and device, electronic equipment and storage medium
CN112435091B (en) * 2020-11-23 2024-03-29 百果园技术(新加坡)有限公司 Recommended content selection method, device, equipment and storage medium
CN112232933A (en) * 2020-12-11 2021-01-15 深圳市房多多网络科技有限公司 House source information recommendation method, device, equipment and readable storage medium
CN113763030A (en) * 2021-07-22 2021-12-07 北京房江湖科技有限公司 House resource recommendation method and device, computer program product and storage medium

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107807943A (en) * 2016-09-09 2018-03-16 腾讯科技（深圳）有限公司 Application program recommends method and device
CN108288179A (en) * 2018-01-25 2018-07-17 链家网（北京）科技有限公司 A kind of user preference source of houses computational methods and system

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108053343A (en) * 2017-12-06 2018-05-18 链家网（北京）科技有限公司 A kind of source of houses method for pushing and system
CN108197288B (en) * 2018-01-16 2020-09-01 平安好房（上海）电子商务有限公司 House source pushing method, device, equipment and medium according to user characteristic preference
CN108334568B (en) * 2018-01-16 2020-08-21 平安好房（上海）电子商务有限公司 House resource pushing method, device, equipment and computer readable storage medium
CN108628984A (en) * 2018-04-27 2018-10-09 福建江夏学院 A kind of the fusion search system and method for information of real estate

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107807943A (en) * 2016-09-09 2018-03-16 腾讯科技（深圳）有限公司 Application program recommends method and device
CN108288179A (en) * 2018-01-25 2018-07-17 链家网（北京）科技有限公司 A kind of user preference source of houses computational methods and system

Also Published As

Publication number Publication date
CN109377329A (en) 2019-02-22

Similar Documents

Publication Publication Date Title
CN109377329B (en) House resource recommendation method and device, storage medium and electronic equipment
US9363634B1 (en) Providing context-relevant information to users
CN110704751B (en) Data processing method and device, electronic equipment and storage medium
CN110634047B (en) Method and device for recommending house resources, electronic equipment and storage medium
CN110619076B (en) Search term recommendation method and device, computer and storage medium
CN106202394A (en) The recommendation method and system of text information
CN110362750B (en) Target user determination method, device, electronic equipment and computer readable medium
CN109831532B (en) Data sharing method, device, equipment and medium
CN113986933A (en) Materialized view creating method and device, storage medium and electronic equipment
CN111291258A (en) Recommendation method and device for searching hot words, electronic equipment and readable medium
US20190095536A1 (en) Method and device for content recommendation and computer readable storage medium
US11023962B2 (en) Searching available rental vehicles based on adjustable parameters
CN111044062B (en) Path planning and recommending method and device
EP3253031B1 (en) Searching available rental vehicles based on adjustable parameters
CN112836128A (en) Information recommendation method, device, equipment and storage medium
CN111798251A (en) Verification method and device of house source data and electronic equipment
CN110633411A (en) Method and device for screening house resources, electronic equipment and storage medium
CN113347276B (en) Mobile access system based on GIS
CN111382365B (en) Method and device for outputting information
CN110322039B (en) Click rate estimation method, server and computer readable storage medium
CN111597439A (en) Information processing method and device and electronic equipment
CN111597438B (en) House source information display method and device and electronic equipment
CN116541421B (en) Address query information generation method and device, electronic equipment and computer medium
CN110619087B (en) Method and apparatus for processing information
CN113486068A (en) Method and device for determining point of interest data, electronic equipment and computer readable medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
TR01 Transfer of patent right

Effective date of registration: 20230421

Address after: Room 802, Information Building, 13 Linyin North Street, Pinggu District, Beijing, 101299

Patentee after: Beijing youzhuju Network Technology Co.,Ltd.

Address before: 100086 no.179, 15th floor, block B, building 1, No.38, Zhongguancun Street, Haidian District, Beijing

Patentee before: BEIJING SHIGUANG RENRAN TECHNOLOGY Co.,Ltd.

TR01 Transfer of patent right