CN105488233A - Reading information recommendation method and system - Google Patents
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- CN105488233A CN105488233A CN201610050871.3A CN201610050871A CN105488233A CN 105488233 A CN105488233 A CN 105488233A CN 201610050871 A CN201610050871 A CN 201610050871A CN 105488233 A CN105488233 A CN 105488233A
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Abstract
The invention relates to a reading information recommendation method and system. The method comprises steps as follows: acquiring historical reading data of a target user, wherein the historical reading data comprise content information about a reading website accessed by the target user through an intelligent terminal; acquiring interesting information of the target user according to the historical reading data; constructing a similar user group according to the interesting information of the target user to collect other users, related to the interesting information of the target user, in the reading website, and acquiring interesting information of other users in the similar user group; generating recommendation information of the target user according to the interesting information of the target user and the interesting information of other users; recommending the recommendation information to the target user. With the adoption of the reading information recommendation method and system, the reading information recommendation accuracy is improved, and the reading information recommendation diversity is realized.
Description
Technical field
The present invention relates to Internet technical field, particularly relate to a kind of reading information recommend method and system.
Background technology
Along with the fast development of Internet technology, information in internet is also presenting like blast and is increasing, the information content scale of society datumization is increasing, and the approach of obtaining information is also more and more wider, the various information resources that people always can find oneself to need in internet.
But the overload of information also can cause us cannot obtain the information and resource that oneself need most efficiently from internet.Under the historical background of large data, the mode of people's obtaining information there occurs huge change.Mostly the push technology of current majority is the similarity based on commodity or information, the shortcoming of such development is that eye can be confined to much-sought-after item by increasing user or heat is searched in information, and those not popular may be that the interested commodity of user or information will be left in the basket.
Reading electronic book more and more becomes a kind of reading method of main flow under the tide of internet, people obtain by the read resource that reading website is recommended the information self needed, and portable with it, the easy acquisition of e-book and instantaneity have won the favor of numerous reading fan.But mostly traditional electronic reading website is to come to user's recommended article content according to the history reading information of the user of this reading website of access, and the recommendation information content causing this user to receive is more single, cannot meet multifarious demand.
Summary of the invention
Based on this, be necessary that the recommendation information content for existing information recommendation mode is more single, the technical matters of multifarious demand cannot be met, a kind of reading information recommend method and system are provided.
A kind of reading information recommend method, comprises the steps:
Obtain the history read data of targeted customer; Wherein, described history read data comprises the content information of described targeted customer by intelligent terminal access reading website;
The information of interest of described targeted customer is obtained according to described history read data;
Information of interest according to described targeted customer builds similar users group, for collecting other users relevant to the information of interest of described targeted customer in described reading website, and obtains the information of interest of other users in described similar users group;
According to the information of interest of described targeted customer and the information of interest of other users, generate the recommendation information of targeted customer;
Described recommendation information is recommended to described targeted customer.
A kind of reading information commending system, comprising:
First acquisition module, for obtaining the history read data of targeted customer; Wherein, described history read data comprises the content information of described targeted customer by intelligent terminal access reading website;
Second acquisition module, for obtaining the information of interest of described targeted customer according to described history read data;
Build module, similar users group is built for the information of interest according to described targeted customer, for collecting other users relevant to the information of interest of described targeted customer in described reading website, and obtain the information of interest of other users in described similar users group;
Generation module, for according to the information of interest of described targeted customer and the information of interest of other users, generates the recommendation information of targeted customer;
Recommending module, for recommending described recommendation information to described targeted customer.
Above-mentioned reading information recommend method and system, by the information of interest of other users in the information of interest of targeted customer and similar users group is combined, not only recommend the information interested to it to described targeted customer, improve the accuracy of described recommended article information; And, also recommend the information of interest of other users in similar users group to described targeted customer, achieve the diversity of described recommended article information.
Accompanying drawing explanation
Fig. 1 is the reading information recommend method process flow diagram of one embodiment of the present of invention;
Fig. 2 is the reading information commending system structural representation of one embodiment of the present of invention.
Embodiment
In order to further set forth the technological means that the present invention takes and the effect obtained, below in conjunction with accompanying drawing and preferred embodiment, to technical scheme of the present invention, carry out clear and complete description.
As shown in Figure 1, Fig. 1 is the reading information recommend method process flow diagram of one embodiment of the present of invention, comprises the steps:
Step S101: the history read data obtaining targeted customer; Wherein, described history read data comprises the content information of described targeted customer by intelligent terminal access reading website;
In this step, according to the content information of described targeted customer by intelligent terminal access reading website, such as, described targeted customer accesses which category information in which reading website by intelligent terminal, and read which content, comment on or paid close attention to which content information etc. in addition.
Step S102: the information of interest obtaining described targeted customer according to described history read data;
In this step, according to history read data as above, the information of interest of described targeted customer is obtained.The information of interest of the targeted customer described in the present invention comprises reading website that targeted customer often accessed by intelligent terminal, which class article that certain reading website is often read etc.
Step S103: the information of interest according to described targeted customer builds similar users group, for collecting other users relevant to the information of interest of described targeted customer in described reading website, and obtain the information of interest of other users in described similar users group;
In this step, by building similar users group, other users of identical information of interest are had to collect by with described targeted customer.Certainly, these users in similar users group are except having except identical information of interest with described targeted customer, also all there is respective information of interest, by obtaining the information of interest of other users in described similar users group, effectively expanding the scope of the information of interest of targeted customer.
Step S1014: the recommendation information generating targeted customer according to the information of interest of described targeted customer and the information of interest of other users;
In this step, consider that targeted customer may with targeted customer except having aforesaid identical information of interest, also there is the consistent information of interest with the information of interest of other users, in order to expand the range of choice of described targeted customer's recommendation information, the information of interest of described targeted customer and the information of interest of other users are combined, generates the recommendation information that targeted customer is final.By aforesaid operations, make reading information recommend method of the present invention, not only recommend the information of interest of targeted customer to targeted customer, improve the accuracy of recommendation information; And, also recommend with the information of interest that targeted customer has other users of identical information of interest to targeted customer, expand the range of choice of targeted customer's recommendation information, achieve the diversity of recommendation information.
Step S105: described recommendation information is recommended to described targeted customer.
In this step, according to the recommendation information of above-mentioned generation, and by described recommendation information recommendation targeted customer, make described targeted customer both can read oneself interested information, also other reading informations relevant with self can be read, widen reading information scope, enhance the diversity of reading information.
Above-mentioned reading information recommend method, by the information of interest of other users in the information of interest of targeted customer and similar users group is combined, not only recommend the information interested to it to described targeted customer, improve the accuracy of described recommended article information; And, also recommend the information of interest of other users in similar users group to described targeted customer, achieve the diversity of described recommended article information.
In actual applications, for user, reading no doubt is topmost demand, if but push reading information in the time of user's sleep for it, then such recommendation effect may will be run counter to desire, so not only need to know what reading information user likes, yet need the reading behavior information knowing user simultaneously, personalized recommendation service could be provided for it like this.
Wherein in an embodiment, reading information recommend method of the present invention, can also comprise: obtain the historical behavior information of targeted customer by intelligent terminal access reading website;
The reading behavior feature of targeted customer according to described historical behavior acquisition of information, and according to described reading behavior feature, described recommendation information is recommended to described targeted customer.
Historical behavior information described in above-described embodiment refers to the behavioural habits of described targeted customer by intelligent terminal access reading website, such as, when which class reading website is section access, which time period to read any the class books on any class reading website in, and at the reading cycle etc. of each time period.The reading behavior feature of targeted customer according to described historical behavior acquisition of information, i.e. the reading time section of described targeted customer in preset time period, history frequency of reading and reading liveness etc.After the above-mentioned reading behavior feature getting described targeted customer, when to targeted customer's recommended article information, just can carry out recommended article information in conjunction with above-mentioned reading behavior feature.By aforesaid operations, achieve the personalized recommendation to described targeted customer.
In actual applications, can to read by user's one week interest preference, history the data that domination time preference and user read access websites preference 3 dimensions every day and screen the personalized recommendation list generating user.
One week interest preference: screen personalized recommendation result, filters out the books of the category that to match with user's one week interesting data, and using the recommended book of books the highest for resource score value in personalized recommendation result as user.
History reads domination time preference every day: formulate the books personalized recommendation entry that timing pushes.
User preference access websites: according to the preference of user to website visiting, formulates the electronic reading class website personalized push entry with this bibliography to it.
Wherein in an embodiment, reading information recommend method of the present invention, after the step of the history read data of described acquisition targeted customer, can also comprise:
Privacy transcoding and integrality process are carried out to described history read data.
Above-mentioned reading information recommend method, by carrying out the process of privacy transcoding to the history read data obtained, protects the reading privacy information of targeted customer to a certain extent; By carrying out integrality process to the history read data obtained, making the history read data obtained more accurately, more fully reflect the history reading behavior of targeted customer, being conducive to improving the accuracy to targeted customer's recommended article information.
Wherein in an embodiment, reading information recommend method of the present invention, described information of interest can comprise interested books category;
The described step obtaining the information of interest of described targeted customer according to described content information comprises:
According to described content information, obtain the interested books category of described targeted customer;
The information of interest of the described information of interest according to described targeted customer and other users generates the recommendation information of targeted customer, is recommended by described recommendation information to comprise to the step of described targeted customer:
The books category recommendation information of targeted customer is generated according to the interested books category of described targeted customer and the interested books category of other users;
Described recommendation by described recommendation information comprises to the step of described targeted customer:
Described books category recommendation information is recommended to described targeted customer.
In the above-described embodiments, reading information recommend method of the present invention, by obtaining targeted customer's interested books category and the interested books category of other users, and the books category recommendation information of targeted customer is generated according to described targeted customer interested books category and the interested books category of other users, described books category recommendation information is recommended to described targeted customer.By such scheme, not only recommend its interested books category to targeted customer, improve the accuracy of recommended article information; And, also recommend targeted customer by with the interested books category of other users that targeted customer has an identical information of interest, achieve the diversity of recommended article information.
In actual applications, the books category described in information recommendation method of the present invention includes but not limited to: city describing love affairs, pass through that illusion, fantasy are magical, swordsman celestial chivalrous, game sports, romantic youth, historical military, clever different suspense, masterpiece biography, science fiction, video display drama, fashionable life, officialdom job market, pursue a goal with determination, educate social sciences and short essay through pipe.
Wherein in an embodiment, reading information recommend method of the present invention, described recommendation by described books category recommendation information can comprise to the step of described targeted customer:
According to the books category recommendation information of described targeted customer, filter out the books matched with described books category recommendation information, and according to the sequence of matching degree height, generate read books recommendation list;
According to described read books recommendation list, to described targeted customer's recommended article books.
In the above-described embodiments, reading information recommend method of the present invention, by filtering out the books matched with described books category recommendation information, and according to the sequence of matching degree height, generates read books recommendation list.When to targeted customer's recommended book, carry out recommended article books according to matching degree order from high to low to targeted customer, achieve the personalization to described targeted customer, precision is recommended.
In actual applications, also can screen by extracting data critical word the books matched with described books category recommendation information, concrete operations are as follows:
(1) data critical word is extracted, according to the most basic classification text vocabulary, Data Matching is carried out to " keyword " field in reading website book information data, and carry out occurrence number statistics, obtain " keyword " field in each different classes of books list corresponding.
(2) " keyword " that match is arranged, filter out former the keywords that occurrence number is the highest, and it can be used as the screening keyword of these classification books.
(3) screening rule is formulated: 1) if comprise screening keyword in the title of books, then think that it is such other books.2) if occur in the brief introduction of books, the number of screening keyword is greater than a certain amount of, then think that it is these classification books.
After above data processing, by carrying out Data Analysis to targeted customer by the url visit data of intelligent terminal access reading website, obtain history read data and the reading behavior information of targeted customer.When obtaining the information of interest of targeted customer, the access url of user is resolved to the history read data of reading; By reading the domination time every day, reading domination time, liveness, burst, Memorability 5 behavioral indicator data weekly, the reading behavior information of targeted customer is described.
Wherein in an embodiment, reading information recommend method of the present invention, before described step of to be recommended described recommendation information to described targeted customer, also comprises:
Determine the identity characteristic information of described targeted customer;
Described recommendation by described recommendation information comprises to the step of described targeted customer:
According to described identity characteristic information, to described targeted customer's recommendation information.
In the above-described embodiments, reading information recommend method of the present invention, before targeted customer's recommended article information, the identity characteristic information of described targeted customer can also be determined, such as, the industry that described targeted customer is engaged in, residing rank etc., then according to described identity characteristic information, to described targeted customer's recommendation information, furthermore achieved that the personalization to targeted customer and precision recommendation.
In actual applications, reading the demand of product operator on sales service for meeting intelligent terminal, before personalized recommendation is carried out to user, can screen the behavior of user or identity characteristic, therefrom delete to select and there is a clearly distinctive class user, personalized recommendation is carried out to it.Concrete screening rule is as follows:
The user with identical occupation is screened;
Screened reading mastery time period identical user every day by setting-up time section;
Screened reading weekly mastery time period identical user by setting-up time section;
By setting threshold value, the user that history frequency of reading is greater than or less than threshold value is screened;
By setting threshold value, the user that liveness in history browing record is greater than or less than threshold value is screened;
Carry out to the user that liveness in front January browing record is greater than or less than threshold value calculations by setting threshold value to select.
By utilizing above-mentioned screening rule, can realize precisely recommending the personalization of particular group.
In actual applications, reading information recommend method of the present invention, can also be captured the behavioral data of targeted customer from web by web crawlers, Data Source is mainly mobile phone reading website and similar reading website.Wherein, behavioral data comprises:
1. book information data.Title, books ID, author, item name, category IDs, front cover picture, brief introduction, price (comprise entity book price, e-book price, by chapter purchasing price, read bag coverage condition), the link of number of words, chapters and sections number, key word, homepage, the link of first chapter and web site name.
2. evaluation index data.Index name, index weigh effect explanation, index calculating method and index calculate data bore.
3. reading behavior data.User ID, date, reading PV, books ID.
4. browing record data.User's reading histories PV records, the login of user, paying/monthly payment, order, client use.
5. subscriber's account and basic document.The ARPU value of user, user's basic document (sex, birthday, occupation), user's communication/note are detailed, user's value-added service uses.
6. user accesses url data.Url address, access time, return data packet length and access locations.
According to above-mentioned behavioral data, targetedly to targeted customer's recommended article information, the personalized recommendation of reading information can be realized.
In actual applications, reading information recommend method of the present invention, can also by using proposed algorithm to the above-mentioned behavioral data of targeted customer, and extract the entry that whole resource score value is not 0, and not by books that user selected, it is carried out descending sort according to resource score value, namely generates the personalized recommendation list of this user.
As shown in Figure 2, Fig. 2 is the reading information commending system structural representation of one embodiment of the present of invention, comprising:
First acquisition module 101, for obtaining the history read data of targeted customer; Wherein, described history read data comprises the content information of described targeted customer by intelligent terminal access reading website;
In the present embodiment, first acquisition module 101 of reading information commending system of the present invention is according to the content information of described targeted customer by intelligent terminal access reading website, such as, described targeted customer accesses which category information in which reading website by intelligent terminal, and read which content, comment on or paid close attention to which content information etc. in addition.
Second acquisition module 102, for obtaining the information of interest of described targeted customer according to described history read data;
In the present embodiment, the second acquisition module 102 of reading information commending system of the present invention, according to history read data as above, obtains the information of interest of described targeted customer.The information of interest of the targeted customer described in the present invention comprises reading website that targeted customer often accessed by intelligent terminal, which class article that certain reading website is often read etc.
Build module 103, similar users group is built for the information of interest according to described targeted customer, for collecting other users relevant to the information of interest of described targeted customer in described reading website, and obtain the information of interest of other users in described similar users group;
In the present embodiment, the structure module 103 of reading information commending system of the present invention, by building similar users group, has other users of identical information of interest to collect by with described targeted customer.Certainly, these users in similar users group are except having except identical information of interest with described targeted customer, also all there is respective information of interest, by obtaining the information of interest of other users in described similar users group, effectively expanding the scope of the information of interest of targeted customer.
Generation module 104, for generating the recommendation information of targeted customer according to the information of interest of described targeted customer and the information of interest of other users;
In the present embodiment, consider that targeted customer may with targeted customer except having aforesaid identical information of interest, also there is the consistent information of interest with the information of interest of other users, in order to expand the range of choice of described targeted customer's recommendation information, the information of interest of described targeted customer and the information of interest of other users combine by the generation module 104 of reading information commending system of the present invention, generate the recommendation information that targeted customer is final.By aforesaid operations, make reading information commending system of the present invention, not only recommend the information of interest of targeted customer to targeted customer, improve the accuracy of recommendation information; And, also recommend with the information of interest that targeted customer has other users of identical information of interest to targeted customer, expand the range of choice of targeted customer's recommendation information, achieve the diversity of recommendation information.
Recommending module 105, for recommending described recommendation information to described targeted customer.
The recommending module 105 of reading information commending system of the present invention is according to the recommendation information of above-mentioned generation, and by described recommendation information recommendation targeted customer, make described targeted customer both can read oneself interested information, also other reading informations relevant with self can be read, widen reading information scope, enhance the diversity of reading information.
Above-mentioned reading information commending system, by the information of interest of other users in the information of interest of targeted customer and similar users group is combined, not only recommend the information interested to it to described targeted customer, improve the accuracy of described recommended article information; And, also recommend the information of interest of other users in similar users group to described targeted customer, achieve the diversity of described recommended article information.
Wherein in an embodiment, reading information commending system of the present invention, described first acquisition module 101 can also be used for: obtain the behavioural information of described targeted customer by intelligent terminal access reading website;
Described second acquisition module 102 can also be used for: the reading behavior feature obtaining described targeted customer according to described behavioural information;
Described recommending module 104, can also be used for: recommend described recommendation information to described targeted customer according to described reading behavior feature.
Historical behavior information described in above-described embodiment refers to the behavioural habits of described targeted customer by intelligent terminal access reading website, such as, when which class reading website is section access, which time period to read any the class books on any class reading website in, and at the reading cycle etc. of each time period.The reading behavior feature of targeted customer according to described historical behavior acquisition of information, i.e. the reading time section of described targeted customer in preset time period, history frequency of reading and reading liveness etc.After the above-mentioned reading behavior feature getting described targeted customer, when to targeted customer's recommended article information, just can carry out recommended article information in conjunction with above-mentioned reading behavior feature.By aforesaid operations, achieve the personalized recommendation to described targeted customer.
Wherein in an embodiment, reading information commending system of the present invention, the reading behavior feature of described targeted customer comprises:
The reading time section of described targeted customer in preset time period, history frequency of reading and reading liveness.
Wherein in an embodiment, reading information commending system of the present invention, after described first acquisition module 101, also comprises:
Pretreatment module, for carrying out privacy transcoding and integrality process to described history read data.
Above-mentioned reading information commending system, carries out the process of privacy transcoding by described pretreatment module to the history read data obtained, protects the reading privacy information of targeted customer to a certain extent; By described pretreatment module, integrality process is carried out to the history read data obtained, make the history read data obtained more accurately, more fully reflect the history reading behavior of targeted customer, be conducive to improving the accuracy to targeted customer's recommended article information.
Wherein in an embodiment, reading information commending system of the present invention, described information of interest comprises interested books category;
Described second acquisition module 102 can also be used for according to described content information, obtains the interested books category of described targeted customer;
Described generation module 103 can also be used for the interested books category according to described targeted customer and the interested books category of other users, generates the books category recommendation information of targeted customer;
Described recommending module 105 can also be used for described books category recommendation information to recommend to described targeted customer.
In the above-described embodiments, reading information commending system of the present invention, by obtaining targeted customer's interested books category and the interested books category of other users, and the books category recommendation information of targeted customer is generated according to described targeted customer interested books category and the interested books category of other users, described books category recommendation information is recommended to described targeted customer.By such scheme, reading information commending system of the present invention not only recommends its interested books category to targeted customer, improves the accuracy of recommended article information; And, also recommend targeted customer by with the interested books category of other users that targeted customer has an identical information of interest, achieve the diversity of recommended article information.
Wherein in an embodiment, reading information commending system of the present invention, described recommending module 105 can also be used for: according to the books category recommendation information of described targeted customer, filter out the books matched with described books category recommendation information, and according to the sequence of matching degree height, generate read books recommendation list;
According to described read books recommendation list, to described targeted customer's recommended article books.
In the above-described embodiments, reading information commending system of the present invention, by filtering out the books matched with described books category recommendation information, and according to the sequence of matching degree height, generates read books recommendation list.When to targeted customer's recommended book, carry out recommended article books according to matching degree order from high to low to targeted customer, achieve the personalization to described targeted customer, precision is recommended.
Wherein in an embodiment, reading information commending system of the present invention, before described recommending module 105, can also comprise: identity determination module, for determining the identity characteristic information of described targeted customer;
Described recommending module 105 can also be used for: according to described identity characteristic information, to described targeted customer's recommendation information.
In the above-described embodiments, reading information commending system of the present invention, before targeted customer's recommended article information, the identity characteristic information of described targeted customer can also be determined by identity determination module, such as, the industry that described targeted customer is engaged in, residing rank etc., then according to described identity characteristic information, to described targeted customer's recommendation information, furthermore achieved that the personalization to targeted customer and precision recommendation.
Above-mentioned reading information commending system, by the information of interest of other users in the information of interest of targeted customer and similar users group is combined, not only recommend the information interested to it to described targeted customer, improve the accuracy of described recommended article information; And, also recommend the information of interest of other users in similar users group to described targeted customer, achieve the diversity of described recommended article information.
Each technical characteristic of the above embodiment can combine arbitrarily, for making description succinct, the all possible combination of each technical characteristic in above-described embodiment is not all described, but, as long as the combination of these technical characteristics does not exist contradiction, be all considered to be the scope that this instructions is recorded.
The above embodiment only have expressed several embodiment of the present invention, and it describes comparatively concrete and detailed, but can not therefore be construed as limiting the scope of the patent.It should be pointed out that for the person of ordinary skill of the art, without departing from the inventive concept of the premise, can also make some distortion and improvement, these all belong to protection scope of the present invention.Therefore, the protection domain of patent of the present invention should be as the criterion with claims.
Claims (10)
1. a reading information recommend method, is characterized in that, comprises the steps:
Obtain the history read data of targeted customer; Wherein, described history read data comprises the content information of described targeted customer by intelligent terminal access reading website;
The information of interest of described targeted customer is obtained according to described history read data;
Information of interest according to described targeted customer builds similar users group, for collecting other users relevant to the information of interest of described targeted customer in described reading website, and obtains the information of interest of other users in described similar users group;
The recommendation information of targeted customer is generated according to the information of interest of described targeted customer and the information of interest of other users;
Described recommendation information is recommended to described targeted customer.
2. reading information recommend method according to claim 1, is characterized in that, also comprise: obtain the historical behavior information of targeted customer by intelligent terminal access reading website;
The reading behavior feature of targeted customer according to described historical behavior acquisition of information, and according to described reading behavior feature, described recommendation information is recommended to described targeted customer.
3. reading information recommend method according to claim 2, is characterized in that, the reading behavior feature of described targeted customer comprises:
The reading time section of described targeted customer in preset time period, history frequency of reading and reading liveness.
4. reading information recommend method according to claim 1, is characterized in that, after the step of the history read data of described acquisition targeted customer, also comprises:
Privacy transcoding and integrality pre-service are carried out to described history read data.
5. reading information recommend method according to claim 1, is characterized in that, described information of interest comprises interested books category;
The described step obtaining the information of interest of described targeted customer according to described content information comprises:
According to described content information, obtain the interested books category of described targeted customer;
The step of the recommendation information of the information of interest generation targeted customer of the described information of interest according to described targeted customer and other users comprises:
According to the interested books category of described targeted customer and the interested books category of other users, generate the books category recommendation information of targeted customer;
Described recommendation by described recommendation information comprises to the step of described targeted customer:
Described books category recommendation information is recommended to described targeted customer.
6. reading information recommend method according to claim 5, is characterized in that, described recommendation by described books category recommendation information comprises to the step of described targeted customer:
According to the books category recommendation information of described targeted customer, filter out the books matched with described books category recommendation information, and according to the sequence of matching degree height, generate read books recommendation list;
According to described read books recommendation list, to described targeted customer's recommended article books.
7. reading information recommend method according to claim 1, is characterized in that, before described step of to be recommended described recommendation information to described targeted customer, also comprises:
Determine the identity characteristic information of described targeted customer;
Described recommendation by described recommendation information comprises to the step of described targeted customer:
According to described identity characteristic information, to described targeted customer's recommendation information.
8. a reading information commending system, is characterized in that, comprising:
First acquisition module, for obtaining the history read data of targeted customer; Wherein, described history read data comprises the content information of described targeted customer by intelligent terminal access reading website;
Second acquisition module, for obtaining the information of interest of described targeted customer according to described history read data;
Build module, similar users group is built for the information of interest according to described targeted customer, for collecting other users relevant to the information of interest of described targeted customer in described reading website, and obtain the information of interest of other users in described similar users group;
Generation module, for generating the recommendation information of targeted customer according to the information of interest of described targeted customer and the information of interest of other users;
Recommending module, for recommending described recommendation information to described targeted customer.
9. reading information commending system according to claim 8, is characterized in that, described first acquisition module, also for: obtain described targeted customer by intelligent terminal access reading website historical behavior information;
Described second acquisition module, also for according to the described historical behavior acquisition of information reading behavior feature of targeted customer.
10. reading information commending system according to claim 9, is characterized in that, described recommending module, also for:
According to described reading behavior feature, described recommendation information is recommended to described targeted customer.
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Cited By (30)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106339469A (en) * | 2016-08-29 | 2017-01-18 | 乐视控股（北京）有限公司 | Method and device for recommending data |
| CN106453585A (en) * | 2016-10-31 | 2017-02-22 | 努比亚技术有限公司 | Information pushing method and information pushing adapter |
| CN106611050A (en) * | 2016-12-22 | 2017-05-03 | 掌阅科技股份有限公司 | Electronic book recommendation method and device, and server |
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| WO2019090741A1 (en) * | 2017-11-10 | 2019-05-16 | 深圳市华阅文化传媒有限公司 | Method and apparatus for recommending books for user to read |
| CN107943871A (en) * | 2017-11-10 | 2018-04-20 | 深圳市华阅文化传媒有限公司 | Recommended user reads the method and device of the books of hobby |
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