CN104504098A - Information recommending method and device - Google Patents
Information recommending method and device Download PDFInfo
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- CN104504098A CN104504098A CN201410838298.3A CN201410838298A CN104504098A CN 104504098 A CN104504098 A CN 104504098A CN 201410838298 A CN201410838298 A CN 201410838298A CN 104504098 A CN104504098 A CN 104504098A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/951—Indexing; Web crawling techniques
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Abstract
The invention provides an information recommending method and device. The method comprises the following steps: determining a user group to which users of visiting a website belong; aiming at each determined user group, finding out to-be-recommended information corresponding to the user group from a corresponding relation table which is stored in advance, and calculating the recommending degree of the to-be-recommended information on the user group according to the interest level of the user group on the to-be-recommended information; selecting a set number of to-be-recommended information with a high recommending degree, and recommending to the users. By application of the information recommending method and device, when the to-be-recommended information with a high interest level is recommended to the users, the conversion rate of the product or service indicated by the to-be-recommended information is improved.
Description
Technical field
The present invention relates to Internet technical field, specifically, the present invention relates to information recommendation method and device.
Background technology
Along with the fast development of Internet technology is with universal, increasing businessman or enterprise are by customizing messages (such as, the advertising message of product or service) render to based on internet platform on so that more client can understand and pay close attention to Related product or the service of businessman or enterprise.Along with the continuous expansion of ecommerce scale, commodity number and kind increase fast, and customer need spends a large amount of time just can find the commodity oneself wanting to buy.This process browsing a large amount of irrelevant information causes the consumer be submerged in problem of information overload constantly to be run off, and in order to address this problem, recommended technology arises at the historic moment.
Existing recommended technology, normally according to historical behavior or the user profile of user, excavates the more interested information to be recommended of customer group; Then, the more interested information of this customer group excavated is recommended to this customer group.
But, the present inventor finds, some information to be recommended are due to the description of information and type characteristic, cause most of customer group to the Interest Measure of a certain class information to be recommended all higher than another kind of, such as customer group treats the Interest Measure of recommendation information " he earned 1,000,000 with ten days " generally higher than recommendation information " sale of Beijing fresh flower ".By existing information recommendation method, above-mentioned information to be recommended " he earned 1,000,000 with ten days " is easily caused to be devoted in a large number of users group; And in fact, the most of users in customer group just watch the scene of bustle, can't buy Related product or service, the conversion ratio of the information to be recommended causing businessman or enterprise to be thrown in is lower.
Therefore, be necessary to provide a kind of information recommendation method, effectively information to be recommended can be thrown in in the customer group comprising a large amount of potential consumer, make things convenient for customers on the one hand and find target product or service from information to be recommended, improve on the other hand the conversion ratio of product indicated by information to be recommended or service, and then promote the experience of user.
Summary of the invention
For the defect that above-mentioned prior art exists, the invention provides a kind of information recommendation method and device, can while the information to be recommended of recommending its level of interest high to user, improve the conversion ratio of product indicated by information to be recommended or service.
The invention provides a kind of information recommendation method, comprising:
Determine the customer group belonging to user of access websites;
For each customer group determined, from the mapping table prestored, find the information to be recommended that this customer group is corresponding, and according to the level of interest of this customer group to this information to be recommended, calculate the recommendation degree of this information to be recommended to this customer group;
The information to be recommended choosing the high setting quantity of recommendation degree is recommended to user.
Preferably, described according to the level of interest of this customer group to this information to be recommended, calculate the recommendation degree of this information to be recommended, specifically comprise:
According to the level of interest of this customer group to this information to be recommended, and the online weight of this information to be recommended, calculate the recommendation degree of this information to be recommended.
Preferably, the customer group stored in described mapping table and the corresponding relation of information to be recommended are determined according to following method in advance:
For an information to be recommended, for the often kind of customer group divided in advance, according to the network access behavior of user each in this customer group, determine the level of interest of this customer group to this information to be recommended;
Choose some customer groups that level of interest is high, in described mapping table, record the corresponding relation of the customer group chosen and this information to be recommended.
Preferably, the customer group chosen also is recorded in described mapping table to the level of interest of this information to be recommended.
Preferably, the described access of the network according to user each in this customer group behavior, determine the level of interest of this customer group to this information to be recommended, comprising:
According to the network access behavior of user each in this customer group, count clicking rate and/or the conversion ratio of this customer group pair information relevant to this information to be recommended or product;
According to the clicking rate counted and/or conversion ratio, determine the level of interest of this customer group to this information to be recommended.
According to a further aspect in the invention, additionally provide a kind of information recommending apparatus, comprising:
Customer group determination module, for determine access websites user belonging to customer group;
Recommendation degree computing module, for each customer group determined for described customer group determination module, the information to be recommended that this customer group is corresponding is found from the mapping table prestored, and according to the level of interest of this customer group to this information to be recommended, calculate the recommendation degree of this information to be recommended to this customer group;
Information recommendation module, recommends to user for the information to be recommended choosing the high setting quantity of recommendation degree.
Preferably, described recommendation degree computing module, comprising:
Corresponding informance searches unit, for each customer group determined for described customer group determination module, finds the information to be recommended that this customer group is corresponding from the mapping table prestored;
Recommendation degree computing unit, for for each customer group determined, for the information each to be recommended that this customer group is corresponding, according to the level of interest of this customer group to this information to be recommended, and the online weight of this information to be recommended, calculate the recommendation degree of this information to be recommended.
Preferably, above-mentioned information recommending apparatus also comprises:
Level of interest computing module, for for an information to be recommended, for the often kind of customer group divided in advance, according to the network access behavior of user each in this customer group, determines the level of interest of this customer group to this information to be recommended;
Corresponding relation logging modle, for for an information to be recommended, chooses some customer groups that level of interest is high, records the corresponding relation of the customer group chosen and this information to be recommended in described mapping table.
Preferably, described corresponding relation logging modle also for recording the customer group chosen to the level of interest of this information to be recommended in described mapping table.
Preferably, described level of interest computing module, specifically for the network access behavior according to user each in this customer group, counts clicking rate and/or the conversion ratio of this customer group pair information relevant to this information to be recommended or product; And according to the clicking rate counted and/or conversion ratio, determine the level of interest of this customer group to this information to be recommended.
In the solution of the present invention, for information to be recommended, according to the level of interest of each customer group to this information to be recommended, choose the potential user group of several high customer groups of level of interest as this information to be recommended, and by this information to be recommended with the customer group corresponding stored chosen in mapping table; Based on the corresponding relation of the information to be recommended prestored and customer group, the information to be recommended that customer group belonging to user is corresponding can be found, and according to the level of interest of this customer group to this information to be recommended, calculate the recommendation degree of this information to be recommended to this customer group; Then, the information to be recommended choosing the high setting quantity of recommendation degree is recommended to user.
Like this, for user, the information to be recommended of recommending to it is the information that level of interest is high, recommendation degree is high, improves Consumer's Experience; For the information to be recommended to user's degree of recommendation, this information to be recommended recommendedly gives its potential user group, instead of recommendedly give its more interested user watched the scene of bustle, improve conversion ratio and the clicking rate of the information relevant to information to be recommended or product, namely improve the recommendation validity of information to be recommended.
The aspect that the present invention adds and advantage will part provide in the following description, and these will become obvious from the following description, or be recognized by practice of the present invention.
Accompanying drawing explanation
Fig. 1 is the schematic flow sheet of the defining method of the customer group corresponding with information to be recommended of the embodiment of the present invention;
Fig. 2 is the schematic flow sheet of the information recommendation method of the embodiment of the present invention;
Fig. 3 a, 3b are the configuration diagram of the information recommending apparatus of the embodiment of the present invention;
Fig. 4 is the configuration diagram of the recommendation degree computing module of the embodiment of the present invention.
Embodiment
Be described below in detail embodiments of the invention, the example of described embodiment is shown in the drawings, and wherein same or similar label represents same or similar element or has element that is identical or similar functions from start to finish.Being exemplary below by the embodiment be described with reference to the drawings, only for explaining the present invention, and can not limitation of the present invention being interpreted as.
Those skilled in the art of the present technique are appreciated that unless expressly stated, and singulative used herein " ", " one ", " described " and " being somebody's turn to do " also can comprise plural form.Should be further understood that, the wording used in instructions of the present invention " comprises " and refers to there is described feature, integer, step, operation, element and/or assembly, but does not get rid of and exist or add other features one or more, integer, step, operation, element, assembly and/or their group.Should be appreciated that, when we claim element to be " connected " or " coupling " to another element time, it can be directly connected or coupled to other elements, or also can there is intermediary element.In addition, " connection " used herein or " coupling " can comprise wireless connections or wirelessly to couple.Wording "and/or" used herein comprises one or more whole or arbitrary unit listing item be associated and all combinations.
Those skilled in the art of the present technique are appreciated that unless otherwise defined, and all terms used herein (comprising technical term and scientific terminology), have the meaning identical with the general understanding of the those of ordinary skill in field belonging to the present invention.It should also be understood that, those terms defined in such as general dictionary, should be understood to that there is the meaning consistent with the meaning in the context of prior art, unless and by specific definitions as here, otherwise can not explain by idealized or too formal implication.
The present inventor finds, from the angle of businessman or enterprise, its objective is that the information of being thrown in is thrown in potential user; From the angle of user, it is wished by the message of throwing in is its interested information.And existing information recommendation method, it is mainly from the angle of user, pushes their interested information to user, does not consider the angle of businessman or enterprise.
Therefore, the present inventor considers, can in advance based on the angle of businessman or enterprise, for information to be recommended, according to each customer group divided in advance to the level of interest of this information to be recommended, select several potential user groups of this information to be recommended, and by this information to be recommended with the potential user group corresponding stored determined in mapping table.
On the one hand, follow-up several potential user groups based on information to be recommended in mapping table and correspondence thereof carry out in the process of information recommendation, information to be recommended only recommendedly can give its potential user group, and can not be recommended to other to the interested customer group of this information to be recommended, like this, from the angle of information to be recommended, customer group is screened, the conversion ratio of product indicated by information to be recommended or service can be improved.
On the other hand, after determining the customer group belonging to the user of access websites, information to be recommended corresponding to this customer group can be found from the mapping table prestored, and according to the level of interest of this customer group to this information to be recommended, calculate the recommendation degree of this information to be recommended to this customer group; The information to be recommended choosing the high setting quantity of recommendation degree is recommended to user.
Like this, both met businessman or enterprise for by its information pushing to be recommended to the object of its potential user group, also the object that the information of recommending to user is the information that its interest level is higher can be met, while raising Consumer's Experience, the carrying out of information to be recommended effectively can also be pushed, improve conversion ratio and the clicking rate of information indicated by information to be recommended or product, namely improve the validity of information recommendation.
Technical scheme of the present invention is described in detail below in conjunction with accompanying drawing.
In the embodiment of the present invention, before the user's recommendation information to access websites, in advance for information to be recommended, the customer group that information to be recommended with this is corresponding can be determined, namely determines the potential user group of this information to be recommended.As shown in Figure 1, specifically can step determine by the following method:
S101: for an information to be recommended, for the often kind of customer group divided in advance, according to the network access behavior of user each in this customer group, determines the level of interest of this customer group to this information to be recommended.
Consider that the network access behavior of user can reflect the interest of user usually to a certain extent.Therefore, in this step, for the often kind of customer group divided in advance, according to the network access behavior of user each in this customer group, the level of interest of this customer group to this information to be recommended can be determined.
Further, from the angle of businessman or enterprise, different user pair information relevant to the information to be recommended of throwing in or the clicking rate of product, the difference of conversion ratio, can reflect the difference of different user to the level of interest of this information to be recommended.
Therefore, in the embodiment of the present invention, for an information to be recommended, first according to the network access behavior of user each in this customer group, clicking rate and/or the conversion ratio of this customer group pair information relevant to this information to be recommended or product can be counted.
Wherein, clicking rate refers to that the user in customer group clicks the quantity of this information to be recommended and browses the ratio of this information to be recommended; Conversion ratio refers to quantity that product indicated by information to be recommended of user in customer group or service carry out corresponding operating and the ratio of quantity clicking this information to be recommended.Wherein, corresponding operating includes but not limited to: log in, register, line duration exceedes setting-up time, pay and buy.
Then, according to the clicking rate counted and/or conversion ratio, the level of interest of this customer group to this information to be recommended can be determined.
Particularly, can by counting the clicking rate of this customer group pair information relevant to this information to be recommended or product, as the level of interest of this customer group to this information to be recommended; Or, also can by counting the conversion ratio of this customer group pair information relevant to this information to be recommended or product, as the level of interest of this customer group to this information to be recommended; Or, closer, by counting this customer group pair information relevant to this information to be recommended or the clicking rate of product and the product of conversion ratio, as the level of interest of this customer group to this information to be recommended.
Such as, for information to be recommended " day earns 2000 ", this description due to this information to be recommended is compared and is attracted eyeball, a lot of customer group (such as, old man's customer group, male customer group, female's customer group, User group, workman's customer group, professional white collar customer group, financing customer group, financial customer group etc.) all can be interested in it.
But most of customer group is just more curious to this information to be recommended " day earns 2000 ", is in a kind of situation of watching the scene of bustle; This information to be recommended is not the information that user really needs, and therefore, a lot of customer group can't buy Related product or service.If the advertiser of information to be recommended " day earns 2000 " is rendered to all to its interested customer group, its throw in cost higher, but its throw in after conversion ratio lower, influence power advertiser throw in effect.
Therefore, in order to avoid there is said circumstances, the potential user group of this information to be recommended can be pre-determined out, namely excavating which customer group the most interesting to this information to be recommended " day earns 2000 ".
Particularly, can according to the network of user each in customer group access behavior, carry out counting user group to the clicking rate of the information relevant to this information to be recommended " day earns 2000 " or product and/or conversion ratio; And using the product of the clicking rate that counts and conversion ratio as the level of interest of this customer group to this information to be recommended " day earns 2000 ".
In the embodiment of the present invention, about various customer group, according to the user of technological means known in those skilled in the art to the setting quantity obtained in advance and the user profile of each user, division can be carried out according to preset strategy and obtains.
Such as, according to sex, user can be divided into male customer group and female's customer group; User can be divided into young user group, middle aged customer group, old customer group etc. according to the age; User can be divided into each province/urban consumer group according to region; User can be divided into clothes customer group, electronic product user group, furniture customer group, mother and baby's product user group etc. according to interest.The mode how dividing customer group is varied, and this programme is also not specifically limited, and will not enumerate at this.
S102: for an information to be recommended, chooses some customer groups that level of interest is high, records the corresponding relation of customer group and this information to be recommended chosen in mapping table.
In order to select the information relevant to information to be recommended or the potential user group of product, in this step, for an information to be recommended, the often kind of customer group can determined according to step S101 is to the level of interest of this information to be recommended, select some customer groups that level of interest is high, namely select the potential user group of this information to be recommended.
Particularly, can for often kind of customer group, if the level of interest of this customer group to this information to be recommended exceedes the interest threshold value of setting, then can using the potential user group of this customer group as this information to be recommended.Wherein, interest threshold value rule of thumb can be set in advance by those skilled in the art.
Such as, after being determined that by step S101 each customer group treats the level of interest of recommendation information " day earns 2000 ", level of interest can be exceeded the potential user group of customer group (such as " financing customer group ", " financial customer group ", " professional white collar group ", " male customer group ") as information to be recommended " day earns 2000 " of the interest threshold value of setting.
More preferably, in order to improve the recommendation validity of information to be recommended, in the embodiment of the present invention, also according to the level of interest of often kind of customer group to this information to be recommended, the potential user group of customer group as this information to be recommended of setting quantity can be selected.Particularly, the level of interest of often kind of customer group to this information to be recommended can be sorted, such as, order from high to low sorts, and chooses the customer group that the comes top N potential user group as this information to be recommended.Wherein, N is the natural number that those skilled in the art carry out setting in advance.
Like this, after the potential user group selecting information to be recommended, the corresponding relation of customer group and this information to be recommended chosen can be recorded in mapping table, based on the corresponding relation of the customer group stored in above-mentioned mapping table and information to be recommended, effective recommendation of information to be recommended can be carried out to online user so that follow-up.
Such as, for information to be recommended " day earns 2000 ", except potential user group can be excavated by the interest threshold value of setting, the level of interest of each customer group to this information to be recommended " day earns 2000 " can also be sorted, select the potential user group of 3 customer groups that level of interest is the highest (such as, " financing customer group ", " financial customer group ", " professional white collar group ") as this information to be recommended " day earns 2000 "; And in mapping table, record the corresponding relation of " financing customer group ", " financial customer group ", " professional white collar group " and this information to be recommended " day earns 2000 " chosen.
More preferably, the customer group chosen can also be recorded in mapping table to the level of interest of this information to be recommended, so that follow-up, each to be recommended information corresponding with user be sorted, recommend its more interested information to this user, improve the experience of user.
Based on the corresponding relation of above-mentioned information to be recommended and customer group, the invention provides a kind of information recommendation method, as shown in Figure 2, its flow process specifically can comprise the steps:
S201: the customer group belonging to user determining access websites.
In this step, for the user of access websites, the determination of the customer group belonging to it can adopt technological means known in those skilled in the art to carry out division to determine, be not described in detail in this.
S202: for each customer group determined, finds the information to be recommended that this customer group is corresponding from the mapping table prestored.
In this step, based on the mapping table of corresponding relation being previously stored with information to be recommended and customer group, the to be recommended information corresponding with the customer group belonging to user can be determined.Particularly, each customer group can determined for step S201, based on above-mentioned mapping table, finds out each to be recommended information corresponding with this customer group.
In practical application, for the information each to be recommended found out, this customer group belonging to user is the potential user group of this information to be recommended, and namely this user is the potential user of information that information to be recommended to this is relevant or product.That is, if by follow-up screening, this information to be recommended is recommended gives user; So from the angle of information to be recommended, information to be recommended recommendedly gives its potential user group, instead of recommendedly give its more interested user watched the scene of bustle.
Such as, if determine, this user belongs to " financing customer group ", then based on above-mentioned mapping table, can find out the to be recommended information corresponding with " financing customer group " and comprise: " day earns 2000 ".Like this, if follow-up, information to be recommended " day earns 2000 " is recommended this user, then can meet the recommended requirements of information to be recommended " day earns 2000 ", namely its recommended is its potential user.That is, information to be recommended " day earns 2000 " can be avoided to render to some users watched the scene of bustle, like this, be conducive to the clicking rate and the conversion ratio that improve the product relevant to information to be recommended " day earns 2000 " or information.
Further, in order to screen the information to be recommended found, each customer group can also determined for step S201, according to the to be recommended information corresponding with this customer group, determines the level of interest of this customer group to this information to be recommended.
In practical application, this customer group, to the level of interest of this information to be recommended, can be determined according to the network access behavior of the network access behavior of each user comprised before in this customer group and this user.Or, also directly can extract this pre-recorded customer group to the level of interest of this information to be recommended from mapping table.
S203: for each customer group determined, according to the level of interest of this customer group to this information to be recommended, calculates the recommendation degree of this information to be recommended to this customer group.
In this step, for the determined each customer group of step S201, for each to be recommended information corresponding with this customer group, the level of interest of recommendation information can be treated according to this customer group, calculate the recommendation degree of this information to be recommended to this customer group.
Further, online user is being carried out in the process of information recommendation, can also according to the level of interest of this customer group to this information to be recommended, and the online weight of this information to be recommended, calculate the recommendation degree of this information to be recommended.Wherein, online weight can by those skilled in the art according to each information to be recommended actual propelling movement value, avail information, businessman bid etc. preset.
S204: the information to be recommended choosing the high setting quantity of recommendation degree is recommended to user.
In order to improve Consumer's Experience, recommending its real interested information to user, according to the recommendation degree of the determined each information to be recommended of step S203, choosing of information to be recommended can be carried out.
Particularly, can according to each customer group and each to be recommended information corresponding with customer group to the recommendation degree of customer group, the information to be recommended selecting the high setting quantity of recommendation degree is recommended to user.
Preferably, for the determined each customer group of step S201, for each to be recommended information corresponding with this customer group, if the recommendation degree of this information to be recommended to this customer group exceedes the recommendation threshold value of setting, then can using the to be recommended information of this information to be recommended as pre-recommendation corresponding to this customer group.Then, the recommendation degree according to the information to be recommended of pre-recommendation corresponding to each customer group sorts, and the information to be recommended choosing the high setting quantity of recommendation degree is recommended to user.
Like this, for user, the information to be recommended of recommending to it is the information that level of interest is high, recommendation degree is high, improves Consumer's Experience; For the information to be recommended to user's degree of recommendation, customer group belonging to this user is the potential user group of this information to be recommended, namely this user is the potential user of information that information to be recommended to this is relevant or product, conversion ratio and the clicking rate of information indicated by information to be recommended or product can be improved, namely improve the recommendation validity of information to be recommended.
Based on above-mentioned information recommendation method, a kind of information recommending apparatus that the embodiment of the present invention provides, as shown in Figure 3 a, specifically can comprise: customer group determination module 301, recommendation degree computing module 302 and information recommendation module 303.
Wherein, customer group determination module 301 for determine access websites user belonging to customer group.
The each customer group of recommendation degree computing module 302 for determining for customer group determination module 301, the information to be recommended that this customer group is corresponding is found from the mapping table prestored, and according to the level of interest of this customer group to this information to be recommended, calculate the recommendation degree of this information to be recommended to this customer group.
Particularly, degree computing module 302 is recommended for the determined each customer group of customer group determination module 301, several information to be recommended that this customer group is corresponding can be found from the mapping table prestored.
In the embodiment of the present invention, after recommendation degree computing module 302 searches information to be recommended corresponding to customer group, for the information each to be recommended found, according to the level of interest of this customer group to this information to be recommended, the recommendation degree of this information to be recommended to this customer group can be calculated.Such as, recommend degree computing module 302 can according to the level of interest of this customer group to this information to be recommended, and the online weight of this information to be recommended, calculate the recommendation degree of this information to be recommended.
Wherein, online weight can by those skilled in the art according to each information to be recommended actual propelling movement value, avail information, businessman bid etc. preset.
In practical application, customer group treats the level of interest of recommendation information, can determine according to the network access behavior of the network access behavior of each user comprised before in this customer group and this user.Or, also directly can extract this pre-recorded customer group to the level of interest of this information to be recommended from mapping table.
Information recommendation module 303 is recommended to user for the information to be recommended choosing the high setting quantity of recommendation degree.
Particularly, information recommendation module 303 can for each customer group, for each to be recommended information corresponding with this customer group, if the recommendation degree of this information to be recommended to this customer group exceedes the recommendation threshold value of setting, then using the to be recommended information of this information to be recommended as pre-recommendation corresponding to this customer group.Then, information recommendation module 303 sorts according to the recommendation degree of the information to be recommended of pre-recommendation corresponding to each customer group, and the information to be recommended choosing the high setting quantity of recommendation degree is recommended to user.
Further, in the embodiment of the present invention, as shown in Figure 3 b, information recommending apparatus can also comprise: level of interest computing module 304, corresponding relation logging modle 305.
Level of interest computing module 304, for for an information to be recommended, for the often kind of customer group divided in advance, according to the network access behavior of user each in this customer group, determines the level of interest of this customer group to this information to be recommended.
Particularly, level of interest computing module 304 according to the network access behavior of user each in this customer group, can count clicking rate and/or the conversion ratio of this customer group pair information relevant to this information to be recommended or product; And according to the clicking rate counted and/or conversion ratio, determine the level of interest of this customer group to this information to be recommended.
Corresponding relation logging modle 305, for for an information to be recommended, chooses some customer groups that level of interest is high, records the corresponding relation of customer group and this information to be recommended chosen in mapping table.
More preferably, corresponding relation logging modle also for recording the customer group chosen to the level of interest of this information to be recommended in mapping table.
In the embodiment of the present invention, as shown in Figure 4, degree computing module 302 is recommended specifically can to comprise: corresponding informance is searched unit 401, recommended degree computing unit 402.
Wherein, corresponding informance searches each customer group of unit 401 for determining for customer group determination module 301, finds the information to be recommended that this customer group is corresponding from the mapping table prestored.
Recommendation degree computing unit 402 is for for each customer group, for the information each to be recommended that this customer group is corresponding, according to the level of interest of this customer group to this information to be recommended, and the online weight of this information to be recommended, calculate the recommendation degree of this information to be recommended.Wherein, online weight can by those skilled in the art according to each information to be recommended actual propelling movement value, avail information, businessman bid etc. preset
In practical application, search for the information each to be recommended that unit 401 finds out for corresponding informance, this customer group belonging to user belongs to the potential user group of this information to be recommended, and namely this user is the potential user of information that information to be recommended to this is relevant or product.
That is, if by the screening of follow-up recommending module 303, information to be recommended is recommended gives user for this; So from the angle of information to be recommended, information to be recommended recommendedly gives its potential user group, instead of recommendedly give its more interested user watched the scene of bustle.
Like this, for user, the information to be recommended of recommending to it is the information that level of interest is high, recommendation degree is high, improves Consumer's Experience; For the information to be recommended to user's degree of recommendation, this information to be recommended recommendedly gives its potential user group, instead of recommendedly give its more interested user watched the scene of bustle, be conducive to the conversion ratio and the clicking rate that improve the information relevant to information to be recommended or product, namely improve the recommendation validity of information to be recommended.
In the embodiment of the present invention, above-mentioned customer group determination module 301, recommend degree computing module 302, the concrete methods of realizing of the function of information recommendation module 303, level of interest computing module 304 and corresponding relation logging modle 305, with reference to the particular content of above-mentioned method flow step as shown in Figure 1 and Figure 2, can repeat no more herein.
Those skilled in the art of the present technique are appreciated that the one or more equipment that the present invention includes and relate to for performing in operation described in the application.These equipment for required object and specialized designs and manufacture, or also can comprise the known device in multi-purpose computer.These equipment have storage computer program within it, and these computer programs optionally activate or reconstruct.Such computer program can be stored in equipment (such as, computing machine) in computer-readable recording medium or be stored in and be suitable for store electrons instruction and be coupled in the medium of any type of bus respectively, described computer-readable medium includes but not limited to that the dish of any type (comprises floppy disk, hard disk, CD, CD-ROM, and magneto-optic disk), ROM (Read-Only Memory, ROM (read-only memory)), RAM (Random Access Memory, storer immediately), EPROM (Erasable Programmable Read-Only Memory, Erarable Programmable Read only Memory), EEPROM (Electrically Erasable ProgrammableRead-Only Memory, EEPROM (Electrically Erasable Programmable Read Only Memo)), flash memory, magnetic card or light card.Namely, computer-readable recording medium comprises and being stored or any medium of transmission information with the form that can read by equipment (such as, computing machine).
The above is only the preferred embodiment of the present invention; it should be pointed out that for those skilled in the art, under the premise without departing from the principles of the invention; can also make some improvements and modifications, these improvements and modifications also should be considered as protection scope of the present invention.
Claims (10)
1. an information recommendation method, is characterized in that, comprising:
Determine the customer group belonging to user of access websites;
For each customer group determined, from the mapping table prestored, find the information to be recommended that this customer group is corresponding, and according to the level of interest of this customer group to this information to be recommended, calculate the recommendation degree of this information to be recommended to this customer group;
The information to be recommended choosing the high setting quantity of recommendation degree is recommended to user.
2. the method for claim 1, is characterized in that, described according to the level of interest of this customer group to this information to be recommended, calculates the recommendation degree of this information to be recommended, specifically comprises:
According to the level of interest of this customer group to this information to be recommended, and the online weight of this information to be recommended, calculate the recommendation degree of this information to be recommended.
3. method as claimed in claim 1 or 2, it is characterized in that, the corresponding relation of the customer group stored in described mapping table and information to be recommended is determined according to following method in advance:
For an information to be recommended, for the often kind of customer group divided in advance, according to the network access behavior of user each in this customer group, determine the level of interest of this customer group to this information to be recommended;
Choose some customer groups that level of interest is high, in described mapping table, record the corresponding relation of the customer group chosen and this information to be recommended.
4. method as claimed in claim 3, is characterized in that, also records the customer group chosen to the level of interest of this information to be recommended in described mapping table.
5. method as claimed in claim 3, is characterized in that, the described access of the network according to user each in this customer group behavior, determines the level of interest of this customer group to this information to be recommended, comprising:
According to the network access behavior of user each in this customer group, count clicking rate and/or the conversion ratio of this customer group pair information relevant to this information to be recommended or product;
According to the clicking rate counted and/or conversion ratio, determine the level of interest of this customer group to this information to be recommended.
6. an information recommending apparatus, is characterized in that, comprising:
Customer group determination module, for determine access websites user belonging to customer group;
Recommendation degree computing module, for each customer group determined for described customer group determination module, the information to be recommended that this customer group is corresponding is found from the mapping table prestored, and according to the level of interest of this customer group to this information to be recommended, calculate the recommendation degree of this information to be recommended to this customer group;
Information recommendation module, recommends to user for the information to be recommended choosing the high setting quantity of recommendation degree.
7. device as claimed in claim 6, it is characterized in that, described recommendation degree computing module, comprising:
Corresponding informance searches unit, for each customer group determined for described customer group determination module, finds the information to be recommended that this customer group is corresponding from the mapping table prestored;
Recommendation degree computing unit, for for each customer group determined, for the information each to be recommended that this customer group is corresponding, according to the level of interest of this customer group to this information to be recommended, and the online weight of this information to be recommended, calculate the recommendation degree of this information to be recommended.
8. device as claimed in claims 6 or 7, is characterized in that, also comprise:
Level of interest computing module, for for an information to be recommended, for the often kind of customer group divided in advance, according to the network access behavior of user each in this customer group, determines the level of interest of this customer group to this information to be recommended;
Corresponding relation logging modle, for for an information to be recommended, chooses some customer groups that level of interest is high, records the corresponding relation of the customer group chosen and this information to be recommended in described mapping table.
9. device as claimed in claim 8, is characterized in that,
Described corresponding relation logging modle also for recording the customer group chosen to the level of interest of this information to be recommended in described mapping table.
10. device as claimed in claim 8, is characterized in that,
Described level of interest computing module, specifically for the network access behavior according to user each in this customer group, counts clicking rate and/or the conversion ratio of this customer group pair information relevant to this information to be recommended or product; And according to the clicking rate counted and/or conversion ratio, determine the level of interest of this customer group to this information to be recommended.
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