CN106649682A - Book friend recommendation method and device - Google Patents
Book friend recommendation method and device Download PDFInfo
- Publication number
- CN106649682A CN106649682A CN201611163342.0A CN201611163342A CN106649682A CN 106649682 A CN106649682 A CN 106649682A CN 201611163342 A CN201611163342 A CN 201611163342A CN 106649682 A CN106649682 A CN 106649682A
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- Prior art keywords
- user
- targeted customer
- book
- reading information
- information
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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
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
Abstract
The invention discloses a book friend commendation method and a device. The corresponding user assemblage of areas that the target users belong to is determined according to location information of target users. Reading information of the user assemblage and the target users is obtained. Book friends to be recommended are determined and the target users are recommended according to the reading information of the user assemblage and the target users that has been obtained. The book friend recommendation method and the device also simultaneously disclose a book friend commendation device.
Description
Technical field
The present invention relates to information recommendation technology in internet, more particularly to a kind of book friend's recommendation method and device.
Background technology
At present, generally checking book review by way of adding book friend, addition manner is passive；And, even if to same
Books or same piece article are commented on, and the books or article that can not represent reader to comment are interested.Obviously, by looking into
The method that book review is read to add book friend not necessarily has same or like hobby.Additionally, adding book by checking book review
Friend, the geographical position between book friend is also not necessarily close, and difference geographically may affect reading classification and the reading of reader
Scope etc..Therefore, checking book review by way of add book friend, can cause book friend between similarity degree it is relatively low, recommendation
Breath is inaccurate.
The content of the invention
In view of this, the embodiment of the present invention expects that providing a kind of book friend recommends method and device, can be based on position recommendation
Friend, improves the accuracy of recommendation information.
To reach above-mentioned purpose, what the technical scheme of the embodiment of the present invention was realized in：
The embodiment of the present invention provides a kind of book friend recommendation method, and methods described includes：
According to the positional information of targeted customer, the corresponding user's set of targeted customer's affiliated area is determined；
The reading information of user's set and the reading information of targeted customer are obtained, according to the reading letter of the user's set for obtaining
The reading information of breath and targeted customer determines book friend to be recommended, and recommends to the targeted customer.
Preferably, the positional information according to targeted customer, determines the corresponding user of targeted customer's affiliated area
Before set, methods described also includes：Preserve the positional information and reading information of each user.
Preferably, the positional information according to targeted customer, determines the corresponding user of targeted customer's affiliated area
Set includes：According to the positional information of targeted customer, the region comprising the targeted customer delimited, by all in defined area
User's composition user's set；
Or more than one region is divided in advance according to geographical position, by all users in targeted customer's affiliated area
Composition user's set.
Preferably, it is described to determine that book friend to be recommended includes：Distance-taxis radix, gender sorting cardinal sum according to user be
No concern sequence radix determines book friend to be recommended with the weighted sum of respective proportion product.
The embodiment of the present invention also provides a kind of book friend recommendation apparatus, and described device includes：Mould is recommended in determining module and acquisition
Block；Wherein,
The determining module, for according to the positional information of targeted customer, determining targeted customer's affiliated area correspondence
User set；
The acquisition recommending module, for obtaining the reading information of user's set and the reading information of targeted customer, according to
The reading information of user's set and the reading information of targeted customer of acquisition determines book friend to be recommended, and pushes away to the targeted customer
Recommend.
Preferably, described device also includes preserving module, for it is determined that the corresponding use of targeted customer's affiliated area
Before the set of family, the positional information and reading information of each user is preserved.
Preferably, the determining module, specifically for the positional information according to targeted customer, delimit and used comprising the target
The region at family, by all users in defined area user's set is constituted；
Or,
Described device also includes division module, for dividing more than one region in advance according to geographical position；
Accordingly, the determining module, specifically for according to more than one region for dividing in advance, by the targeted customer
All users composition user's set in affiliated area.
Preferably, the acquisition recommending module, specifically for the distance-taxis radix according to user, gender sorting cardinal sum
Whether concern sequence radix determines book friend to be recommended with the weighted sum of respective proportion product.
Book friend provided in an embodiment of the present invention recommends method and device, according to the positional information of targeted customer, it is determined that described
The corresponding user's set of targeted customer's affiliated area；Obtain the reading information of user's set and the reading information of targeted customer, root
Determine book friend to be recommended according to the reading information of user's set and the reading information of targeted customer that obtain, and to the targeted customer
Recommend.That is, the embodiment of the present invention can directly by each user read when positional information and reading information carry out
Preserve, as the basis that book friend recommends；Also, according to the positional information of targeted customer, delimit the area comprising the targeted customer
Domain, by all users in defined area user's set is constituted, or according to the advance geographic area for dividing, by the targeted customer
All users composition user's set in affiliated area；When needing to targeted customer's recommendation friend, it is possible to used according to target
The current positional information in family, determines the corresponding user's set of targeted customer's affiliated area, and obtains the reading information of user's set
And the reading information of targeted customer；According to the reading information and the reading information of targeted customer of the user's set for obtaining, it is determined that treating
Recommendation friend, and recommended to targeted customer, the embodiment of the present invention determines book to be recommended based on positional information and reading information
Friend, improves the similarity degree between book friend, so as to improve the accuracy of recommendation information.
Description of the drawings
Fig. 1 is the schematic flow sheet of book friend's recommendation method of the embodiment of the present invention；
Fig. 2 is the detailed process schematic diagram of book friend's recommendation method of the embodiment of the present invention；
Fig. 3 is the composition structural representation of book friend's recommendation apparatus of the embodiment of the present invention.
Specific embodiment
The characteristics of in order to more fully hereinafter understand the embodiment of the present invention and technology contents, below in conjunction with the accompanying drawings to this
The realization of bright embodiment is described in detail, appended accompanying drawing purposes of discussion only for reference, not for limiting the present invention.
In the embodiment of the present invention, according to the positional information of targeted customer, determine that targeted customer's affiliated area is corresponding
User gathers；The reading information of user's set and the reading information of targeted customer are obtained, according to the reading of the user's set for obtaining
The reading information of information and targeted customer determines book friend to be recommended, and recommends to the targeted customer.
Here, user's set can be the positional information according to targeted customer, delimit comprising the targeted customer
Region, by all users in defined area user's set is constituted；Can also divide one previously according to geographical position
Area above, by all users in targeted customer's affiliated area user's set is constituted；
Wherein, it is described to delimit the region comprising the targeted customer, can delimit one centered on the targeted customer
Individual geographical coverage area, such as：Using radius it is geographic area in 2 kilometer ranges as drawing centered on the targeted customer
Determine region；In practical application, it is also possible to not centered on the targeted customer, as long as the targeted customer is in defined area
Can, specifically how to divide and do not limit.
User set reading information include user set in each user reading information, each user read when,
The positional information and reading information of the user will be preserved, as the reading information included by user's owning user set it
One.
Wherein it is determined that before the corresponding user's set of targeted customer's affiliated area, the position of each user can be preserved
Information and reading information.
Here, the positional information for preserving each user includes：The positional information of each user and reading information are preserved
In the reading information of the corresponding user's set of targeted customer's affiliated area.
Further, it is described to determine that book friend to be recommended includes：Distance-taxis radix, gender sorting cardinal sum according to user
Whether concern sequence radix determines book friend to be recommended with the weighted sum of respective proportion product.
In the embodiment of the present invention, book friend's recommendation method realizes flow process as shown in figure 1, comprising the following steps：
Step 101：According to the positional information of targeted customer, the corresponding user's collection of targeted customer's affiliated area is determined
Close；
Here, before user's set is determined according to the positional information of targeted customer, first have to obtain and preserve user's
Positional information and reading information.The reading information of user's set includes the reading information of each user in user's set, each use
Family will preserve the positional information and reading information of the user, as included by user's owning user set when reading
One of reading information.
Wherein, the reading information, can be user's collection, the information of the books of purchase, or the figure read
The information of book, or user comment, the information of books paid close attention to.
Here it is possible to pass through base station, Wireless Fidelity (Wireless Fidelity, wifi), global positioning system
The modes such as (Global Positioning System, GPS), big-dipper satellite positioning obtain the positional information of user.The position of user
Confidence breath is directly preserved or is stored in different regions；Wherein, the region is to be divided in advance according to geographical position, such as：1
Area, 2nd area, 3rd area etc..
Further, server can be with the positional information of every user of timing acquisition, it is also possible to obtain when user searches for
The positional information of the user.
Here, in the case where the positional information and reading information of each user are directly preserved, according to the position of targeted customer
Confidence ceases, and delimits the region comprising the targeted customer, and by all users in defined area user's set is constituted；Can also be
More than one region is divided previously according to geographical position, by all users in targeted customer's affiliated area use is constituted
Gather at family.Wherein, the positional information and reading information of each user is maintained in server.
Step 102：The reading information of user's set and the reading information of targeted customer are obtained, according to the user's set for obtaining
Reading information and the reading information of targeted customer determine book to be recommended friend, and recommend to the targeted customer.
Here, the book information in the bookshelf of every user, such as：The book that possesses, the book of purchase and the book read
Relevant information；And the reading information such as book review, concern, record is left in the server.These records can be pushed away as book friend
The basis recommended.
Further, the books of identical (classification) can be co-owned according to user, bought were read (just jointly
Reading) books of identical (classification) or the conditions such as (concern) (same category) books were commented on jointly, further to sentence
Whether disconnected user possesses similar reading interest.Such as：50% (more than or equal to 3) books will be possessed (read) and belong to same
Two users of one type (author) are defined as possessing similar reading interest.
According to the reading information and the reading information of targeted customer of user's set, determining has similar reading emerging between user
After interest, the sequence of book friend to be recommended can be determined by each attribute key element of user.
It is described below and the concrete grammar of book friend to be recommended is determined by each attribute key element of user：
Here, the attribute key element of book friend mainly includes：The distance between user, user's sex, user whether pay close attention to books,
The stolen book number of times of user, user gradation.Wherein, the weight of each attribute key element is runed on backstage, and affects to issue list
Foreground shows result.
Further, in user distance-taxis radix, gender sorting cardinal sum whether pay close attention to sequence radix with it is each
Book friend to be recommended is determined from the weighted sum of proportion product.Result of calculation retains 2 significant digits.
Specifically, the book friend sequence=distance-taxis radix * 50%+ gender sorting radix * 30%+ to be recommended whether rows of concern
Sequence radix * 20%；
Specifically, it is as follows for the discrimination standard of distance-taxis radix：In the case where distance is less than 500 meters, distance row
Sequence radix is 50；In distance in the case of 500 meters to 1000 meters, distance-taxis radix is 40；It is more than 1000 meters in distance
In the case of, distance-taxis radix is 10.
It is as follows for the discrimination standard of gender sorting radix：In the case of the opposite sex, gender sorting radix is 60；In the same sex
In the case of, gender sorting radix is 15；In the case where sex is unknown, gender sorting radix is 35.
It is as follows for the discrimination standard for whether paying close attention to sequence radix：In the case where not paying close attention to, if concern sequence radix
For 90；In the case where having focused on, if concern sequence radix is 10.
Here, the use of user property key element can be preferred process during the determination of book to be recommended friend's sequence, but not
It is well-determined；The weight of each attribute key element of user is obtained by data statistics, is not unmodifiable.
Wherein it is possible to targeted customer's recommendation friend by way of text prompt.Specifically, during user can be gathered
Book to be recommended friend list as prompting content.
Below further concrete introduction is done to the technical scheme of book provided in an embodiment of the present invention friend's recommendation method.
As shown in Fig. 2 the detailed process of the book friend's recommendation method for the embodiment of the present invention.Concrete steps include：
Step 21：Obtain and preserve the positional information and reading information of user；
Here, obtain and preserve the basis that the positional information and reading information of user are recommended as book friend.
Wherein, the reading information, can be the information of books that user possesses, buys, or the figure read
The information of book, or user comment, the information of books paid close attention to.
The positional information of user can obtain the position of user by modes such as base station, wifi, GPS, big-dipper satellite positioning
Information.The positional information of user is directly preserved or is stored in different regions；Wherein, the region is to be carried according to geographical position
Front division, such as：1st area, 2nd area, 3rd area etc..
Further, server can be with the positional information of every user of timing acquisition, it is also possible to obtain when user searches for
The positional information of the user.
Step 22：Obtain the positional information of targeted customer；
Here, before to targeted customer's recommendation friend, first have to obtain the positional information and reading information of targeted customer
And it is saved in server.
Step 23：Determine that user gathers；
Here, according to the positional information of targeted customer and the positional information of user and reading in server are had been saved in
Information determines that user gathers.Specifically, here, in the case where the positional information and reading information of each user are directly preserved,
According to the positional information of targeted customer, the region comprising the targeted customer delimited, be made up of all users in defined area
User gathers；Can also divide more than one region previously according to geographical position, by targeted customer's affiliated area
Interior all users composition user's set.
Step 24：According to the reading information and the reading information of targeted customer of user's set, determine book to be recommended friend and to
Targeted customer recommends.
Here, the book information in the bookshelf of every user, such as：The book that possesses, the book of purchase and the book read
Relevant information；And the reading information such as book review, concern, record is left in the server.These records can be pushed away as book friend
The basis recommended.
Further, the books of identical (classification) can be co-owned according to user, bought were read (just jointly
Reading) books of identical (classification) or (concern) (same category) books were commented on jointly judging whether user gathers around
There is similar reading interest.Such as：50% (more than or equal to 3) books will be possessed (read) and belong to same type (author)
Two users be defined as possessing similar reading interest.
Determine have similar reading emerging between user according to the reading information of user's set and the reading information of targeted customer
After interest, the sequence of book friend to be recommended can be determined by each attribute key element of user.
It is described below and the concrete grammar of book friend to be recommended is determined by each attribute key element of user：
Here, the attribute key element of book friend mainly includes：The distance between user, user's sex, user whether pay close attention to books,
The stolen book number of times of user, user gradation.Wherein, the weight of each attribute key element is runed on backstage, and affects to issue list
Foreground shows result.
Further, in user distance-taxis radix, gender sorting cardinal sum whether pay close attention to sequence radix with it is each
Book friend to be recommended is determined from the weighted sum of proportion product.Result of calculation retains 2 significant digits.
Specifically, the book friend sequence=distance-taxis radix * 50%+ gender sorting radix * 30%+ to be recommended whether rows of concern
Sequence radix * 20%.
Specifically, it is as follows for the discrimination standard of distance-taxis radix：In the case where distance is less than 500 meters, distance row
Sequence radix is 50；In distance in the case of 500 meters to 1000 meters, distance-taxis radix is 40；It is more than 1000 meters in distance
In the case of, distance-taxis radix is 10.
It is as follows for the discrimination standard of gender sorting radix：In the case of the opposite sex, gender sorting radix is 60；In the same sex
In the case of, gender sorting radix is 15；In the case where sex is unknown, gender sorting radix is 35.
It is as follows for the discrimination standard for whether paying close attention to sequence radix：In the case where not paying close attention to, if concern sequence radix
For 90；In the case where having focused on, if concern sequence radix is 10.
Here, the use of user property key element can be preferred process during the determination of book to be recommended friend's sequence, but not
It is well-determined.The weight of each attribute key element of user is obtained by data statistics, is not unmodifiable.
Wherein it is possible to targeted customer's recommendation friend by way of text prompt.Specifically, during user can be gathered
Book to be recommended friend list as prompting content.
To realize above-mentioned book friend recommendation method, the embodiment of the present invention additionally provides a kind of book friend recommendation apparatus, described device
Composition structural representation as shown in figure 3, including：Determining module 31 and acquisition recommending module 32；Wherein,
The determining module 31, for according to the positional information of targeted customer, determining targeted customer's affiliated area pair
The user's set answered；
The acquisition recommending module 32, for obtaining the reading information of user's set and the reading information of targeted customer, root
Determine book friend to be recommended according to the reading information of user's set and the reading information of targeted customer that obtain, and to the targeted customer
Recommend.
Further, described device also includes preserving module, for it is determined that targeted customer's affiliated area is corresponding
Before user's set, the positional information and reading information of each user is preserved.
Here, the determining module 31, specifically for the positional information according to targeted customer, delimit and used comprising the target
The region at family, by all users in defined area user's set is constituted；
Or,
Described device also includes division module, for dividing more than one region in advance according to geographical position；
Accordingly, the determining module 31, specifically for according to more than one region for dividing in advance, being used by the target
All users composition user's set in the affiliated area of family.
The preserving module, specifically for the positional information and reading information of the targeted customer are stored in into targeted customer
In the reading information of the corresponding user's set of affiliated area.
Wherein, the acquisition recommending module 32, specifically for the distance-taxis radix according to user, gender sorting cardinal sum
Whether concern sequence radix determines book friend to be recommended with the weighted sum of respective proportion product.
In actual applications, the determining module 31, acquisition recommending module 32, preserving module and division module can be by positions
Central processing unit (CPU, Central Processing Unit) in mobile terminal, microprocessor (MPU, Micro
Processor Unit), digital signal processor (DSP, Digital Signal Processor) or field-programmable gate array
Row (FPGA, Field Programmable Gate Array) etc. are realized.
The above, only presently preferred embodiments of the present invention is not intended to limit protection scope of the present invention, it is all
Any modification, equivalent and improvement for being made within the spirit and principles in the present invention etc., should be included in the protection of the present invention
Within the scope of.
Claims (8)
1. a kind of book friend recommendation method, it is characterised in that methods described includes：
According to the positional information of targeted customer, the corresponding user's set of targeted customer's affiliated area is determined；
Obtain user set reading information and targeted customer reading information, according to obtain user set reading information and
The reading information of targeted customer determines book friend to be recommended, and recommends to the targeted customer.
2. method according to claim 1, it is characterised in that the positional information according to targeted customer, it is determined that described
Before the corresponding user's set of targeted customer's affiliated area, methods described also includes：Preserve positional information and the reading of each user
Information.
3. method according to claim 1 and 2, it is characterised in that the positional information according to targeted customer, determines institute
Stating the corresponding user's set of targeted customer's affiliated area includes：According to the positional information of targeted customer, delimit and include the target
The region of user, by all users in defined area user's set is constituted；
Or more than one region is divided in advance according to geographical position, it is made up of all users in targeted customer's affiliated area
User gathers.
4. method according to claim 1 and 2, it is characterised in that the determination book friend to be recommended include：According to user's
It is to be recommended to determine with the weighted sum of respective proportion product whether distance-taxis radix, gender sorting cardinal sum pay close attention to sequence radix
Book friend.
5. a kind of book friend recommendation apparatus, it is characterised in that described device includes：Determining module and acquisition recommending module；Wherein,
The determining module, for according to the positional information of targeted customer, determining the corresponding use of targeted customer's affiliated area
Gather at family；
The acquisition recommending module, for obtaining the reading information of user's set and the reading information of targeted customer, according to acquisition
The reading information of user's set and the reading information of targeted customer determine book to be recommended friend, and recommend to the targeted customer.
6. device according to claim 5, it is characterised in that described device also includes preserving module, for it is determined that institute
Before stating the corresponding user's set of targeted customer's affiliated area, the positional information and reading information of each user are preserved.
7. the device according to claim 5 or 6, it is characterised in that
The determining module, specifically for the positional information according to targeted customer, delimit the region comprising the targeted customer, by
All users composition user's set in defined area；
Or,
Described device also includes division module, for dividing more than one region in advance according to geographical position；
Accordingly, the determining module, specifically for according to more than one region for dividing in advance, by belonging to the targeted customer
All users composition user's set in region.
8. the device according to claim 5 or 6, it is characterised in that the acquisition recommending module, specifically for according to user
Distance-taxis radix, gender sorting cardinal sum whether pay close attention to sequence radix wait to push away to determine with the weighted sum of respective proportion product
Recommend book friend.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611163342.0A CN106649682A (en) | 2016-12-15 | 2016-12-15 | Book friend recommendation method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611163342.0A CN106649682A (en) | 2016-12-15 | 2016-12-15 | Book friend recommendation method and device |
Publications (1)
| Publication Number | Publication Date |
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| CN106649682A true CN106649682A (en) | 2017-05-10 |
Family
ID=58822933
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611163342.0A Pending CN106649682A (en) | 2016-12-15 | 2016-12-15 | Book friend recommendation method and device |
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100332330A1 (en) * | 2009-06-30 | 2010-12-30 | Google Inc. | Propagating promotional information on a social network |
| CN102663047A (en) * | 2012-03-29 | 2012-09-12 | 中国科学院计算技术研究所 | Method and device for mining social relationship during mobile reading |
| CN103685415A (en) * | 2012-09-20 | 2014-03-26 | 腾讯科技（深圳）有限公司 | User dynamic information acquiring and pushing methods in social network, client and server |
| CN105141508A (en) * | 2015-09-10 | 2015-12-09 | 天津师范大学 | Microblog system friend recommending method based on neighbor relations |
| CN105589966A (en) * | 2015-12-24 | 2016-05-18 | 中山大学深圳研究院 | Friend recommendation method based on composite scores |
| CN106156157A (en) * | 2015-04-15 | 2016-11-23 | 厦门简帛信息科技有限公司 | A kind of e-book navigation system and method |
-
2016
- 2016-12-15 CN CN201611163342.0A patent/CN106649682A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100332330A1 (en) * | 2009-06-30 | 2010-12-30 | Google Inc. | Propagating promotional information on a social network |
| CN102663047A (en) * | 2012-03-29 | 2012-09-12 | 中国科学院计算技术研究所 | Method and device for mining social relationship during mobile reading |
| CN103685415A (en) * | 2012-09-20 | 2014-03-26 | 腾讯科技（深圳）有限公司 | User dynamic information acquiring and pushing methods in social network, client and server |
| CN106156157A (en) * | 2015-04-15 | 2016-11-23 | 厦门简帛信息科技有限公司 | A kind of e-book navigation system and method |
| CN105141508A (en) * | 2015-09-10 | 2015-12-09 | 天津师范大学 | Microblog system friend recommending method based on neighbor relations |
| CN105589966A (en) * | 2015-12-24 | 2016-05-18 | 中山大学深圳研究院 | Friend recommendation method based on composite scores |
Non-Patent Citations (1)
| Title |
|---|
| 夏立新等: "基于SoLoMo的智慧自助图书馆服务体系研究", 《图书情报工作》 * |
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Application publication date: 20170510 |