CN113362094A - Promotion system and method based on user interest points - Google Patents
Promotion system and method based on user interest points Download PDFInfo
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Abstract
The invention discloses a promotion system and a method based on user interest points, which relate to the technical field of promotion and comprise the following steps: the interest point analyzing unit is used for acquiring and analyzing sign-in data and social information of target users in the position social network corresponding to each promotion channel, and determining user interest point information contained in the sign-in data and the social information according to an analysis result; the attribution processing unit is suitable for attributing the user interest point information provided by the interest point analyzing unit so as to determine user identification information of each activated user and a corresponding promotion channel contained in the user interest point information; has the advantages of accurate popularization and high intelligent degree.
Description
Technical Field
The invention relates to the technical field of remote popularization, in particular to a remote conference system and a remote conference method.
Background
The web promotion is a promotion mode that a website is established by taking enterprise products or services as core contents, and then the website is displayed to netizens through various free or charged channels, and the effect of small investment and large return can be achieved by the web promotion.
Common promotion modes are overall promotion, mutual promotion, Baidu promotion (CPC, CPS, CPV, CPA) and the like, and free website promotion modes are new media channel modes such as forums, SNSs, exchange links, B2B platform building stations, blogs, microblogs, WeChats and the like; in narrow sense, the carrier of the network promotion (web promotion) is the internet, and the promotion which leaves the internet is not even the network promotion; can be divided into two types: the user experience of the user is made, and public praise popularization is carried out by utilizing an internet platform tool.
The network popularization and the network marketing are two different concepts, the network marketing is focused on a marketing aspect, whether actual economic benefits are generated after the network marketing is emphasized, the network popularization is focused on the popularization, the website flow, the world ranking, the access amount, the registration amount and the like brought to enterprises after the network marketing are emphasized, and the purpose is to expand the popularity and the influence of popularized objects. It can be said that the step of network promotion must be included in network marketing, and network promotion is the core work of network marketing.
Another easily blurred concept is: and (5) website popularization. The website popularization is an extremely important part in network marketing, the website is a main body of a network, and a lot of network popularization comprises the website popularization. Of course, the network promotion also carries out non-website promotion, such as offline products, companies and the like. The two concepts are easy to be confused because the network promotion activities run through the life cycle of the website, and the network promotion activities are all related to a series of links existing in the website planning, construction, promotion, feedback and the like.
The network advertisement is a means adopted by network popularization. Besides the network advertisement, the network popularization can also be realized by using methods such as a search engine, friend links, network news stir-frying and the like.
With the rapid development of the internet, the number of net citizens will be more and more, and by 12 months in 2010, the number of Chinese net citizens reaches 4.57 hundred million, which is the first to live in the world, so the influence of the network will be larger and larger. If an information island on the internet is not desired, effective network advertising needs to be implemented. For enterprises, network popularization is well done, and economic benefits can be brought; for individuals, more people can know and know more friends.
The existing network popularization system is often operated manually, popularization is not accurate enough, and target crowds cannot be locked accurately.
Disclosure of Invention
In view of this, the present invention provides a promotion system and method based on user interest points, which have the advantages of precise promotion and high degree of intelligence.
In order to achieve the purpose, the invention adopts the following technical scheme:
a promotion system based on user points of interest, comprising: the interest point analyzing unit is used for acquiring and analyzing sign-in data and social information of target users in the position social network corresponding to each promotion channel, and determining user interest point information contained in the sign-in data and the social information according to an analysis result; the attribution processing unit is suitable for attributing the user interest point information provided by the interest point analyzing unit so as to determine user identification information of each activated user and a corresponding promotion channel contained in the user interest point information; and the feedback unit is suitable for respectively determining the user identification information of the activated users corresponding to each promotion channel according to the user identification information of each activated user obtained by the attribution processing unit and the promotion channel corresponding to the user identification information, and feeding back the user identification information to the corresponding promotion channel so as to update the promotion channel.
Further, the interest point analyzing unit acquires check-in data and social information of a target user in a location social network, wherein the check-in data includes at least two check-in locations corresponding to check-in time, and the social information is interaction information between the target user and other users except the target user in the location social network; according to each check-in position, calculating spatial distribution of the interest points to be recommended in the position social network, wherein the spatial distribution comprises a first prediction probability of the interest points to be recommended among the check-in positions; according to the check-in positions and the social information, calculating time distribution of the interest points to be recommended in the position social network, wherein the time distribution comprises second prediction probabilities of the interest points to be recommended in the check-in positions of all the social information; and recommending interest points to the target user according to the first prediction probability and the second prediction probability.
Further, the check-in data and social information of the target user in the location social network include: the channel logs from the popularization channels are suitable for recording user access records of user terminals accessing the popularization channels; and a user log from each user terminal adapted to record user behavior information for each user terminal; the user interest point information specifically includes: analyzing the user log to obtain an activated user list corresponding to the activated user, and analyzing the channel log to obtain channel access information corresponding to each promotion channel; and the attribution processing unit is specifically adapted to: and respectively aiming at each activation user in the activation user list, acquiring user identification information corresponding to the activation user, matching the user identification information with the channel access information corresponding to each promotion channel, and determining the promotion channel corresponding to the activation user according to a matching result.
Further, the user access record of each user terminal accessing the promotion channel includes at least one of the following: user identification information, user access time, and access type, wherein the access type includes: click type, download type, and/or collection type; the user behavior information of each user terminal includes: clicking, downloading, installing, and/or initiating a behavior and its corresponding time; the user identification information includes at least one of: IDFA information, user ID, device fingerprint, device model, user agent, and IP address. .
A promotion method based on user points of interest, the method performing the steps of: step 1: acquiring and analyzing sign-in data and social information of target users in a position social network corresponding to each promotion channel, and determining user interest point information contained in the sign-in data and the social information according to an analysis result; step 2: attributing the user interest point information to determine user identification information of each activated user and a corresponding promotion channel contained in the user interest point information; and step 3: and the user identification information of the activated users corresponding to each promotion channel is respectively determined according to the obtained user identification information of each activated user and the promotion channel corresponding to the activated user, and the user identification information is fed back to the corresponding promotion channel so as to update the promotion channel.
Further, the check-in data includes at least two check-in locations corresponding to check-in times, and the social information is interaction information between the target user and other users except the target user in the location social network; according to each check-in position, calculating spatial distribution of the interest points to be recommended in the position social network, wherein the spatial distribution comprises a first prediction probability of the interest points to be recommended among the check-in positions; according to the check-in positions and the social information, calculating time distribution of the interest points to be recommended in the position social network, wherein the time distribution comprises second prediction probabilities of the interest points to be recommended in the check-in positions of all the social information; and recommending interest points to the target user according to the first prediction probability and the second prediction probability.
Further, the spatial distribution includes a first predicted probability of the point of interest to be recommended among check-in positions, including: acquiring longitude coordinates and bright coordinates of each check-in position, and generating a spatial check-in list of each check-in position according to the longitude coordinates and the bright coordinates; performing hierarchical clustering on every two check-in positions in the spatial check-in list to obtain at least one check-in interval of the target user; and performing kernel density estimation on each check-in interval, and taking the average value of the results of the kernel density estimation as a first prediction probability of the interest point to be recommended between each check-in position.
Compared with the prior art, the invention has the following beneficial effects: according to the method and the device, the interest points of the user and the related log information files are obtained, so that the interest of the user can be accurately positioned, the promotion objects meeting the promotion target are judged, and the promotion accuracy is guaranteed.
Drawings
The invention is described in further detail below with reference to the following figures and detailed description:
fig. 1 is a schematic system structure diagram of a promotion system based on user interest points disclosed in the embodiment of the present invention.
Fig. 2 is a schematic flow chart of a method for popularizing points based on user interest points according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided for illustrative purposes, and other advantages and effects of the present invention will become apparent to those skilled in the art from the present disclosure.
Please refer to fig. 1. It should be understood that the structures, ratios, sizes, and the like shown in the drawings and described in the specification are only used for matching with the disclosure of the specification, so as to be understood and read by those skilled in the art, and are not used to limit the conditions of the present invention, so that the present invention has no technical significance. In addition, the terms such as "upper", "lower", "left", "right", "middle" and "one" used in the present specification are for clarity of description, and are not intended to limit the scope of the present invention, and changes or modifications in the relative relationship may be made without substantial technical changes and modifications.
Example 1
A promotion system based on user points of interest, comprising: the interest point analyzing unit is used for acquiring and analyzing sign-in data and social information of target users in the position social network corresponding to each promotion channel, and determining user interest point information contained in the sign-in data and the social information according to an analysis result; the attribution processing unit is suitable for attributing the user interest point information provided by the interest point analyzing unit so as to determine user identification information of each activated user and a corresponding promotion channel contained in the user interest point information; and the feedback unit is suitable for respectively determining the user identification information of the activated users corresponding to each promotion channel according to the user identification information of each activated user obtained by the attribution processing unit and the promotion channel corresponding to the user identification information, and feeding back the user identification information to the corresponding promotion channel so as to update the promotion channel.
Specifically, the network promotion tool is for assisting network promotion, so that network promotion becomes easier and more efficient, network marketing software with various functions aiming at different network promotion platforms, such as blog promotion software, forum promotion software, mail marketing promotion software, classified information promotion software and the like, and the promotion modes are combined into one set of software.
However, the promotion tool is easy to form a large amount of junk files in a short time, so the mode needs manual operation, at least 22W elements are required to be invested in each year when enterprises independently do network promotion to build a team, and the answer is positive if professional website management teams exist.
Example 2
On the basis of the previous embodiment, the interest point analyzing unit acquires check-in data and social information of a target user in a location social network, wherein the check-in data comprises at least two check-in locations corresponding to check-in time, and the social information is interaction information between the target user and other users except the target user in the location social network; according to each check-in position, calculating spatial distribution of the interest points to be recommended in the position social network, wherein the spatial distribution comprises a first prediction probability of the interest points to be recommended among the check-in positions; according to the check-in positions and the social information, calculating time distribution of the interest points to be recommended in the position social network, wherein the time distribution comprises second prediction probabilities of the interest points to be recommended in the check-in positions of all the social information; and recommending interest points to the target user according to the first prediction probability and the second prediction probability.
Example 3
On the basis of the previous embodiment, the check-in data and the social information of the target user in the location social network comprise: the channel logs from the popularization channels are suitable for recording user access records of user terminals accessing the popularization channels; and a user log from each user terminal adapted to record user behavior information for each user terminal; the user interest point information specifically includes: analyzing the user log to obtain an activated user list corresponding to the activated user, and analyzing the channel log to obtain channel access information corresponding to each promotion channel; and the attribution processing unit is specifically adapted to: and respectively aiming at each activation user in the activation user list, acquiring user identification information corresponding to the activation user, matching the user identification information with the channel access information corresponding to each promotion channel, and determining the promotion channel corresponding to the activation user according to a matching result.
Specifically, the task of recommending a geographical location of possible interest to a user is called point-of-interest recommendation. The interest point recommendation meets the personalized requirements of the user and alleviates the problem of information overload faced by the user, and on the other hand, the interest point recommendation helps to realize intelligent location services such as location-aware advertising services and the like on the LBSN service, so that the business income of the LBSN service provider is increased. Thus, point of interest recommendations play an increasingly important role in location-based social networks.
Example 4
On the basis of the previous embodiment, the user access record of each user terminal accessing the promotion channel includes at least one of the following: user identification information, user access time, and access type, wherein the access type includes: click type, download type, and/or collection type; the user behavior information of each user terminal includes: clicking, downloading, installing, and/or initiating a behavior and its corresponding time; the user identification information includes at least one of: IDFA information, user ID, device fingerprint, device model, user agent, and IP address.
Example 5
As shown in fig. 2, a promotion method based on user interest points performs the following steps: step 1: acquiring and analyzing sign-in data and social information of target users in a position social network corresponding to each promotion channel, and determining user interest point information contained in the sign-in data and the social information according to an analysis result; step 2: attributing the user interest point information to determine user identification information of each activated user and a corresponding promotion channel contained in the user interest point information; and step 3: and the user identification information of the activated users corresponding to each promotion channel is respectively determined according to the obtained user identification information of each activated user and the promotion channel corresponding to the activated user, and the user identification information is fed back to the corresponding promotion channel so as to update the promotion channel.
Example 6
On the basis of the previous embodiment, the check-in data comprises at least two check-in positions corresponding to check-in time, and the social information is interaction information between the target user and other users except the target user in the position social network; according to each check-in position, calculating spatial distribution of the interest points to be recommended in the position social network, wherein the spatial distribution comprises a first prediction probability of the interest points to be recommended among the check-in positions; according to the check-in positions and the social information, calculating time distribution of the interest points to be recommended in the position social network, wherein the time distribution comprises second prediction probabilities of the interest points to be recommended in the check-in positions of all the social information; and recommending interest points to the target user according to the first prediction probability and the second prediction probability.
Example 7
On the basis of the above embodiment, the spatial distribution includes a first predicted probability of the point of interest to be recommended among check-in positions, including: acquiring longitude coordinates and bright coordinates of each check-in position, and generating a spatial check-in list of each check-in position according to the longitude coordinates and the bright coordinates; performing hierarchical clustering on every two check-in positions in the spatial check-in list to obtain at least one check-in interval of the target user; and performing kernel density estimation on each check-in interval, and taking the average value of the results of the kernel density estimation as a first prediction probability of the interest point to be recommended between each check-in position.
It should be noted that, the system provided in the foregoing embodiment is only illustrated by dividing the functional units, and in practical applications, the functions may be distributed by different functional units according to needs, that is, the units or steps in the embodiments of the present invention are further decomposed or combined, for example, the units in the foregoing embodiment may be combined into one unit, or may be further decomposed into multiple sub-units, so as to complete all or part of the functions described above. The names of the units and steps involved in the embodiments of the present invention are only for distinguishing the units or steps, and are not to be construed as unduly limiting the present invention.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes and related descriptions of the storage unit and the processing unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Those of skill in the art would appreciate that the various illustrative elements, method steps, and steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the software elements, method steps, and corresponding programs may be located in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The terms "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing or implying a particular order or sequence.
The terms "comprises," "comprising," or any other similar term are intended to cover a non-exclusive inclusion, such that a process, method, article, or unit that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or unit.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (7)
1. A promotion system based on user interest points, comprising: the interest point analyzing unit is used for acquiring and analyzing sign-in data and social information of target users in the position social network corresponding to each promotion channel, and determining user interest point information contained in the sign-in data and the social information according to an analysis result; the attribution processing unit is suitable for attributing the user interest point information provided by the interest point analyzing unit so as to determine user identification information of each activated user and a corresponding promotion channel contained in the user interest point information; and the feedback unit is suitable for respectively determining the user identification information of the activated users corresponding to each promotion channel according to the user identification information of each activated user obtained by the attribution processing unit and the promotion channel corresponding to the user identification information, and feeding back the user identification information to the corresponding promotion channel so as to update the promotion channel.
2. The system of claim 1, wherein the POI parsing unit obtains check-in data and social information of a target user in a location social network, wherein the check-in data comprises at least two check-in locations corresponding to check-in times, and the social information is interaction information between the target user and other users except the target user in the location social network; according to each check-in position, calculating spatial distribution of the interest points to be recommended in the position social network, wherein the spatial distribution comprises a first prediction probability of the interest points to be recommended among the check-in positions; according to the check-in positions and the social information, calculating time distribution of the interest points to be recommended in the position social network, wherein the time distribution comprises second prediction probabilities of the interest points to be recommended in the check-in positions of all the social information; and recommending interest points to the target user according to the first prediction probability and the second prediction probability.
3. The system of claim 1, wherein the check-in data and social information of the target user in the location social network comprises: the channel logs from the popularization channels are suitable for recording user access records of user terminals accessing the popularization channels; and a user log from each user terminal adapted to record user behavior information for each user terminal; the user interest point information specifically includes: analyzing the user log to obtain an activated user list corresponding to the activated user, and analyzing the channel log to obtain channel access information corresponding to each promotion channel; and the attribution processing unit is specifically adapted to: and respectively aiming at each activation user in the activation user list, acquiring user identification information corresponding to the activation user, matching the user identification information with the channel access information corresponding to each promotion channel, and determining the promotion channel corresponding to the activation user according to a matching result.
4. The system of claim 3, wherein the user access records of the respective user terminals accessing the promotion channel include at least one of: user identification information, user access time, and access type, wherein the access type includes: click type, download type, and/or collection type; the user behavior information of each user terminal includes: clicking, downloading, installing, and/or initiating a behavior and its corresponding time; the user identification information includes at least one of: IDFA information, user ID, device fingerprint, device model, user agent, and IP address.
5. A promotion method based on user interest points based on the system of any one of claims 1 to 4, characterized in that the method performs the following steps: step 1: acquiring and analyzing sign-in data and social information of target users in a position social network corresponding to each promotion channel, and determining user interest point information contained in the sign-in data and the social information according to an analysis result; step 2: attributing the user interest point information to determine user identification information of each activated user and a corresponding promotion channel contained in the user interest point information; and step 3: and the user identification information of the activated users corresponding to each promotion channel is respectively determined according to the obtained user identification information of each activated user and the promotion channel corresponding to the activated user, and the user identification information is fed back to the corresponding promotion channel so as to update the promotion channel.
6. The method of claim 5, wherein the check-in data includes check-in locations corresponding to at least two check-in times, and the social information is interaction information between the target user and users other than the target user in the location social network; according to each check-in position, calculating spatial distribution of the interest points to be recommended in the position social network, wherein the spatial distribution comprises a first prediction probability of the interest points to be recommended among the check-in positions; according to the check-in positions and the social information, calculating time distribution of the interest points to be recommended in the position social network, wherein the time distribution comprises second prediction probabilities of the interest points to be recommended in the check-in positions of all the social information; and recommending interest points to the target user according to the first prediction probability and the second prediction probability.
7. The method of claim 6, wherein the spatial distribution comprises a first predicted probability of the point of interest to be recommended between check-in locations, comprising: acquiring longitude coordinates and bright coordinates of each check-in position, and generating a spatial check-in list of each check-in position according to the longitude coordinates and the bright coordinates; performing hierarchical clustering on every two check-in positions in the spatial check-in list to obtain at least one check-in interval of the target user; and performing kernel density estimation on each check-in interval, and taking the average value of the results of the kernel density estimation as a first prediction probability of the interest point to be recommended between each check-in position.
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Application publication date: 20210907 |