CN105718576A - Individual position recommending system related to geographical features - Google Patents
Individual position recommending system related to geographical features Download PDFInfo
- Publication number
- CN105718576A CN105718576A CN201610044328.2A CN201610044328A CN105718576A CN 105718576 A CN105718576 A CN 105718576A CN 201610044328 A CN201610044328 A CN 201610044328A CN 105718576 A CN105718576 A CN 105718576A
- Authority
- CN
- China
- Prior art keywords
- user
- geographic
- module
- preference
- location
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000005065 mining Methods 0.000 claims description 26
- 238000004364 calculation method Methods 0.000 claims description 13
- 238000012549 training Methods 0.000 claims description 12
- 239000013598 vector Substances 0.000 claims description 10
- 238000000034 method Methods 0.000 claims description 7
- 238000010586 diagram Methods 0.000 claims description 5
- 238000010276 construction Methods 0.000 claims description 4
- 238000013481 data capture Methods 0.000 claims description 4
- 238000010606 normalization Methods 0.000 claims description 4
- 238000005070 sampling Methods 0.000 claims description 3
- 238000001914 filtration Methods 0.000 abstract description 4
- 230000007547 defect Effects 0.000 abstract description 2
- 238000005516 engineering process Methods 0.000 description 3
- 238000009825 accumulation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000035755 proliferation Effects 0.000 description 1
Classifications
-
- 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
-
- 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
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses an individual position recommending system related to geographical features, and mainly aims to overcome the defects that the conventional collaborative filtering recommendation algorithm is poor in performance in a data sparseness scene. According to the technical scheme, different functional modules are coordinated with one another to achieve recommendation: a database acquiring module is adopted to acquire target information; a user database module is adopted to save target information in a user database; a user preference digging module is adopted to acquire a candidate position recommending list which is ordered according to user preference from a user database; a geographical database module is adopted to store the target information in a geographical database; a geographical feature digging module is adopted to acquire the candidate position recommending list which is ordered according to geographical features from the geographical database; a recommending module is adopted to acquire recommendation results by using the recommending list ordered according to user preference and the recommending list ordered according to geographical feature influence. As geographical features are taken into account, the problem of data sparseness is alleviated, and the individual position recommending system can be used in site push service based on a position social network.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a personalized recommendation technology related to geographic features, which can be used for location push service in a location-based social network.
Background
With the rapid development of database technology and the wide application of database management systems, data accumulation is increasing. In the face of the proliferation of data, it is desirable to mine out much of the important information hidden behind it so that it can be better used to serve people. The service provider has a great deal of personal information and historical records related to the user, and the data can be used for actively recommending related services to the user, so that the service provider is convenient for the user and beneficial to the service provider.
The traditional recommendation system algorithm mainly uses a recommendation algorithm based on collaborative filtering and a recommendation algorithm based on content. The advent and advancement of wireless networks and location-aware technologies has driven the explosion of mobile internet, and particularly location-based social networks, and users' geo-location data has begun to be acquired in large quantities via client GPS. The geographic context begins to be an important feature attribute for location recommendation, enabling some goods recommendation tasks based on the user's geographic location.
After collecting the geographic position data of the user, the user needs to be divided on the geographic position, and the dividing method is based on the longitude and latitude of the location where the user signs in. And then, calculating the prediction scores of the user and the region to the positions through a preset model by scanning the position history records of all the users in the region and all the history records of the users to be recommended, further obtaining candidate positions, and performing recommendation calculation based on model collaborative filtering.
In the currently existing recommendation calculation schemes based on collaborative filtering, one is based on the similarity degree between users and the other is based on the similarity degree between articles.
In the above methods, since no consideration is given to the influence of geographic features on the location recommendation for the user, the performance is poor in a data sparse scene, such as remote location recommendation, and the problem of cold start of a new user and a new article cannot be effectively solved.
Disclosure of Invention
The invention aims to provide a personalized position recommendation system related to geographic features aiming at the defects of the method, and the personalized position recommendation system uses a potential dirichlet allocation topic model in combination with the position category and the geographic features to provide interesting positions for users.
To achieve the above object, a recommendation system of the present invention includes:
the data acquisition module DC is used for capturing the sign-in record of the target website user and providing data for the user database module UD and the geographic database module DD;
the user database module UD is used for storing all information captured by the data capture module DC in a user database by taking the user ID as an index and providing user information for the user preference mining module UM;
the geographic database module DD is used for storing all information captured by the data capture module DC in a geographic database by taking a geographic city as an index and providing geographic information for the geographic feature mining module DM;
a user preference mining module UM for mining the preference of the user by using the data in the user database module UD to obtain a candidate position recommendation List sorted according to the preference of the user1；
A geographic feature mining module DM, configured to mine geographic features using the data in the geographic database module DD to obtain a candidate position recommendation List sorted according to the geographic feature influence2；
A recommendation module RD for recommending a List List based on the candidate locations sorted according to the user's preferences1And a List of candidate location recommendations sorted by geographic feature impact2And generating a final recommendation Result.
The invention has the advantages that:
according to the invention, the user preference mining module learns the personal interests of the user, so that personalized position recommendation is realized;
according to the invention, the geographic characteristic mining module is used for integrating the geographic characteristic influence, so that the problem of data sparsity is relieved, especially the problem of remote location recommendation.
Drawings
FIG. 1 is an overall framework diagram of the recommendation system of the present invention;
FIG. 2 is a block diagram of a user preference mining module framework in the present invention;
FIG. 3 is a block diagram of a geographic feature mining module of the present invention;
FIG. 4 is a diagram of a recommender framework in accordance with the present invention.
Embodiments and effects of the present invention are described in detail below with reference to the accompanying drawings.
Detailed description of the preferred embodiments
The core idea of the invention is to consider the influence of geographic features, combine position categories, use a potential Dirichlet allocation topic model, and provide a personalized position recommendation system related to the geographic features, so as to provide interested positions for users.
Referring to fig. 1, the present invention includes: the system comprises a data acquisition module DC, a user database module UD, a geographic database module DD, a user preference mining module UM, a geographic feature mining module DM and a recommendation module RD. The data acquisition module DC is used for capturing the sign-in record of the target website user and providing data for the user database module UD and the geographic database module DD; the user database module UD is used for storing all information acquired by the data acquisition module DC in a user database by taking the user ID as an index and providing user information for the user preference mining module UM; a user preference mining module UM which uses the user sign-in record in the user database module DC to mine the preference of the user to be recommended and obtain a candidate position recommendation List ordered according to the user preference1(ii) a A geographic database module DD for storing all the information acquired by the data acquisition module DC by taking the geographic city as an indexProviding geographic information for a geographic feature mining module DM in a geographic database; the geographic feature mining module DM uses user check-in records contained in different geographic cities in the geographic database module DD to mine the geographic features of the city to be recommended to obtain a candidate position recommendation List sorted according to the influence of the geographic features2(ii) a Recommendation module RD uses recommendation List List obtained by user preference mining module UM1And a recommendation List obtained by the geographic feature mining module DM2And obtaining a final recommendation result.
The data acquisition module DC acquires a check-in record of a target website user, wherein the check-in record comprises: user ID, semantic location, such as daylotus garden, category of semantic location, such as travel & traffic, and geographic city where the semantic location is, such as west' an. Wherein the category of the semantic location includes: art & entertainment, college & university, event, grocery store, night market, outdoor & leisure, residential, shop & service and travel & traffic.
Referring to fig. 2, the user preference mining module UM includes: subject model training unit UM1Three-layer graph model construction unit UM2And a location preference value calculation unit UM3。
The topic model training Unit UM1The system comprises a user database module UD, a theme characteristic vector UV and a user database module UD, wherein the user database module UD is used for obtaining user check-in records and themes and generating theme characteristic vectors UV corresponding to the themes of each user;
the three-layer graph model construction unit UM2And the user sign-in record and topic model training unit UM is used for obtaining the user sign-in record and the topic model training unit UM according to the user database module UD1Constructing a three-layer graph structure of a user layer, a position layer and a theme layer which are composed of all users, all positions and all themes by using the obtained theme feature vector UV;
the position preference value calculating unit UM3The method is used for obtaining the prior knowledge of the user-subject probability distribution in the user database according to the three-layer graph structure of the user layer, the position layer and the subject layerAnd a priori knowledge β of the topic-location probability distributionvCalculating the preference value f of the user to the position not visiteduvWherein the preference value f of the user to the non-visited locationuvCalculated by the following formula:
wherein,representing user u versus topic zkIs estimated based on the degree of preference of the user,representing a topic z for a location v in a user databasekThe estimation of the probability of generation in (1),representing a topic zkThe number of times sampled in the user u check-in record,indicating that position v is on subject z in user u check-in recordkNumber of times of middle sampling, subscriptIndicating that the subject i, R is not includedrRepresenting the number of topics contained in the check-in record of the user u;
using user preference values f for locationuvThe inaccessible positions are sorted in a descending manner, the first k positions are selected to generate a candidate position recommendation List sorted according to the preference of the user1。
Referring to fig. 3, the geographic feature mining module DM includes: topic model training unit DM1Three-layer graph model building unit DM2And a position preference value calculation unit DM3。
The topic model training unit DM1The system comprises a geographic database module DD, a theme feature vector DV, a user name database module DD, a user name database module;
the three-layer graph model building unit DM2And a module DM for training the check-in records and topic models of all users in the geographic city contained in the geographic database module DD1Constructing a geographic layer consisting of all cities, all positions and all topics, and a three-layer graph structure of a position layer and a topic layer by the obtained topic feature vector DV;
the position preference value calculating unit DM3The method is used for obtaining the prior knowledge of the probability distribution of the geographic city-subject in the geographic database according to the three-layer graph structure of the geographic layer, the position layer and the subject layerAnd a priori knowledge β 'of the topic-location probability distribution'vCalculating the preference value f of the geographic city to the positionlv', wherein the geographic city has a preference value f for locationlv', calculated by the formula:
wherein,representing a geographic city l vs. a topic zkIs estimated based on the degree of preference of the user,representing a location v in a geographic database on a topic zkThe estimation of the probability of generation in (1),representing a topic zkThe number of times sampled in all check-in records for geographic city l,indicating that the position v is on the subject zkNumber of times of middle sampling, subscriptIndicating that the subject i, R is not includedlRepresents the number of topics contained in all check-in records of the geographic city l, | V | represents the number of locations contained in all check-in records of the geographic city l.
Preference value f for location using geographic citieslv' sorting the positions in descending order, selecting the first k positions to form a candidate position recommendation List in order according to the influence of geographic features2。
Referring to fig. 4, the recommendation module RD includes: weighting coefficient calculation submodule RD1And location recommendation submodule RD2。
The weighting coefficient calculation submodule RD1Calculating the weight value omega occupied by the user's personal preference in making a decision by the useruAnd the weight ω occupied by the geographical influence in making the decision by the userlThe calculation formula is as follows:
wherein,mean value, P, representing user u preferenceuRepresents the check-in record for user u,represents the set of locations contained in the check-in record for user u,representing the collection of topics contained by the check-in record for user u,mean value, P, representing the preference of a geographic city llA check-in record representing all users in the geographic city l,represents the set of locations contained in the check-in records of all users in the geographic city l,the set of topics that the check-in records of all users in the geographic city l contain is represented.
The position recommendation submodule RD2For calculating said two recommendation lists List1And List2Weight R of medium position:
R＝λufuv+λlf′lv，
wherein,indicating a normalization of the weights taken up in making the decision on the user's personal preferences,representing the normalization of the weight that the geographic features occupy in making the user's decision.
And sorting the positions according to the height of the weighted value, selecting the first k positions to obtain a recommendation Result, wherein the numerical value of k is related to a specific recommendation scene, and if the position in the city to be recommended is more than 100, taking 20 as k is a reasonable choice.
Claims (10)
1. A personalized location recommendation system related to a geographic feature, comprising:
the data acquisition module DC is used for acquiring the sign-in record of the target website user and providing data for the user database module UD and the geographic database module DD;
the user database module UD is used for storing all information captured by the data capture module DC in a user database by taking the user ID as an index and providing user information for the user preference mining module UM;
the geographic database module DD is used for storing all information captured by the data capture module DC in a geographic database by taking a geographic city as an index and providing geographic information for the geographic feature mining module DM;
a user preference mining module UM for mining the preference of the user by using the data in the user database module UD to obtain a candidate position recommendation List sorted according to the preference of the user1；
A geographic feature mining module DM, configured to mine geographic features using the data in the geographic database module DD to obtain a candidate position recommendation List sorted according to the geographic feature influence2；
A recommendation module RD for recommending a List List based on the candidate locations sorted according to the user's preferences1And a List of candidate location recommendations sorted by geographic feature impact2And generating a final recommendation Result.
2. The system of claim 1, wherein the check-in record comprises: the user ID, the semantic location, the category of the semantic location and the geographic city in which the semantic location is located.
3. The system of claim 1, wherein the categories of semantic locations include art & entertainment, college & university, event, grocery store, night market, outdoor & leisure, residential, shop & service, and travel & traffic.
4. The system according to claim 1, wherein said user preference mining module UM comprises:
subject model training unit UM1The system comprises a user database module UD, a theme characteristic vector UV and a user database module UD, wherein the user database module UD is used for obtaining user check-in records and themes and generating theme characteristic vectors UV corresponding to the themes of each user;
three-layer graph model construction unit UM2And the user sign-in record and topic model training unit UM is used for obtaining the user sign-in record and the topic model training unit UM according to the user database module UD1Constructing the obtained theme characteristic vector UV by all users and all positions respectivelyA three-layer diagram structure of a user layer, a position layer and a theme layer which are formed by all themes;
location preference value calculation unit UM3The method is used for obtaining the prior knowledge of the user-subject probability distribution in the user database according to the three-layer graph structure of the user layer, the position layer and the subject layerAnd a priori knowledge β of the topic-location probability distributionvCalculating the preference value f of the user to the position not visiteduvUsing the user preference value f for locationuvThe inaccessible positions are sorted in a descending manner, the first k positions are selected to generate a candidate position recommendation List sorted according to the preference of the user1。
5. The system according to claim 1, wherein said geographic feature mining module DM comprises:
topic model training unit DM1The system comprises a geographic database module DD, a theme feature vector DV, a user name database module DD, a user name database module;
three-layer graph model construction unit DM2And a module DM for training the check-in records and topic models of all users in the geographic city contained in the geographic database module DD1Constructing a geographic layer consisting of all cities, all positions and all topics, and a three-layer graph structure of a position layer and a topic layer by the obtained topic feature vector DV;
position preference value calculation unit DM3The method is used for obtaining the prior knowledge of the probability distribution of the geographic city-subject in the geographic database according to the three-layer graph structure of the geographic layer, the position layer and the subject layerAnd a priori knowledge β of the topic-location probability distributionv', calculating a geographic city preference value f for locationlv', preference of location using geographic cityValue flv' sorting the positions in descending order, selecting the first k positions to form a candidate position recommendation List in order according to the influence of geographic features2。
6. The system of claim 1, wherein said recommendation module RD comprises:
weighting coefficient calculation submodule RD1: for calculating the weight value omega occupied by the personal preference of the user when the user makes a decision according to the relative standard deviation formulauAnd the weight ω occupied by the geographical influence in making the decision by the userl；
Position recommendation submodule RD2: for calculating the two recommendation lists List according to a weighting formula1And List2And (4) sorting the weighted values of the middle positions according to the height of the weighted values to obtain a recommendation Result.
7. The system according to claim 4, wherein the location preference value calculation unit UM3Calculating the preference value f of the user to the position not visiteduvCalculated by the following formula:
wherein,representing user u versus topic zkIs estimated based on the degree of preference of the user,representing a position v in a user database with respect to a topic zkIs determined based on the estimated probability of generation of,representing a topic zkThe number of times sampled in the check-in record for user u,indicating that location v is about topic z in user u's check-in recordkNumber of times sampled, subscriptIndicating that the subject i, R is not includedrThe number of the topics contained in the check-in record of the user u is represented, and the number of the positions contained in the check-in record of the user is represented by N.
8. The system according to claim 5, wherein the location preference value calculation unit DM3Calculating the preference value f of the geographic city to the positionlv', calculated by the formula:
wherein,representing a geographic city l vs. a topic zkIs estimated based on the degree of preference of the user,representing a location v in a geographic database on a topic zkThe estimation of the probability of generation in (1),representing a topic zkThe number of times sampled in all check-in records for geographic city l,indicating that the position v is on the subject zkNumber of times of middle sampling, subscriptIndicating that the subject i, R is not includedlRepresents the number of topics contained in all check-in records of the geographic city l, | V | represents the number of locations contained in all check-in records of the geographic city l.
9. The system of claim 6, wherein the weighting factor calculation submodule RD1Calculating a weight ω occupied by the user's personal preference in making the decisionuAnd the weight ω occupied by the geographical influence in making the decision by the userlCalculated by the following formula:
wherein,mean value, P, representing user u preferenceuRepresents the check-in record for user u,represents the set of locations contained in the check-in record for user u,representing the collection of topics contained by the check-in record for user u,mean value, P, representing the preference of a geographic city llA check-in record representing all users in the geographic city l,indicating the location contained in the check-in record of all users in the geographic city lIn the collection of the images, the image data is collected,the set of topics that the check-in records of all users in the geographic city l contain is represented.
10. The system of claim 6, wherein the location recommendation submodule RD2Calculating two recommendation lists1And List2The weighted value of the middle position is calculated by the following formula:
R＝λufuv+λlflv′，
wherein,indicating a normalization of the weights taken up in making the decision on the user's personal preferences,representing the normalization of the weight that the geographic features occupy in making the user's decision.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610044328.2A CN105718576B (en) | 2016-01-22 | 2016-01-22 | Personalized location recommender system relevant to geographical feature |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610044328.2A CN105718576B (en) | 2016-01-22 | 2016-01-22 | Personalized location recommender system relevant to geographical feature |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105718576A true CN105718576A (en) | 2016-06-29 |
| CN105718576B CN105718576B (en) | 2019-07-16 |
Family
ID=56153944
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610044328.2A Active CN105718576B (en) | 2016-01-22 | 2016-01-22 | Personalized location recommender system relevant to geographical feature |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105718576B (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106951572A (en) * | 2017-04-12 | 2017-07-14 | 上海斐讯数据通信技术有限公司 | A kind of food and drink commending system and dining recommending method |
| CN107180269A (en) * | 2017-05-27 | 2017-09-19 | 维沃移动通信有限公司 | Recommend method and terminal device in a kind of seat |
| CN107562798A (en) * | 2017-08-02 | 2018-01-09 | 北京大学（天津滨海）新代信息技术研究院 | A kind of real-time information recommendation method of the time geodata based on user |
| CN108009877A (en) * | 2017-11-24 | 2018-05-08 | 阿里巴巴集团控股有限公司 | Information mining method and device |
| CN108268519A (en) * | 2016-12-30 | 2018-07-10 | 阿里巴巴集团控股有限公司 | A kind of method and apparatus of recommendation network object |
| CN108460101A (en) * | 2018-02-05 | 2018-08-28 | 山东师范大学 | Point of interest of the facing position social networks based on geographical location regularization recommends method |
| CN108958247A (en) * | 2018-07-02 | 2018-12-07 | 深圳市益鑫智能科技有限公司 | A kind of guided robot |
| CN109558961A (en) * | 2017-09-25 | 2019-04-02 | 阿里巴巴集团控股有限公司 | Determine method and system, storage medium, processor and the device of location information |
| CN109688178A (en) * | 2017-10-19 | 2019-04-26 | 阿里巴巴集团控股有限公司 | Recommended method, device and equipment |
| CN109902222A (en) * | 2018-11-30 | 2019-06-18 | 华为技术有限公司 | Recommendation method and device |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010067175A (en) * | 2008-09-12 | 2010-03-25 | Toshiba Corp | Hybrid content recommendation server, recommendation system, and recommendation method |
| CN102594905A (en) * | 2012-03-07 | 2012-07-18 | 南京邮电大学 | Method for recommending social network position interest points based on scene |
| CN103064924A (en) * | 2012-12-17 | 2013-04-24 | 浙江鸿程计算机系统有限公司 | Travel destination situation recommendation method based on geotagged photo excavation |
| CN105069717A (en) * | 2015-07-29 | 2015-11-18 | 陕西师范大学 | Personalized travel route recommendation method based on tourist trust |
-
2016
- 2016-01-22 CN CN201610044328.2A patent/CN105718576B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010067175A (en) * | 2008-09-12 | 2010-03-25 | Toshiba Corp | Hybrid content recommendation server, recommendation system, and recommendation method |
| CN102594905A (en) * | 2012-03-07 | 2012-07-18 | 南京邮电大学 | Method for recommending social network position interest points based on scene |
| CN103064924A (en) * | 2012-12-17 | 2013-04-24 | 浙江鸿程计算机系统有限公司 | Travel destination situation recommendation method based on geotagged photo excavation |
| CN105069717A (en) * | 2015-07-29 | 2015-11-18 | 陕西师范大学 | Personalized travel route recommendation method based on tourist trust |
Non-Patent Citations (2)
| Title |
|---|
| 孔晓笛等: "基于分层模型的位置推荐方法研究", 《计算机与数字工程》 * |
| 陈迪等: "在线地图服务日志的大数据分析", 《小型微型计算机系统》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108268519A (en) * | 2016-12-30 | 2018-07-10 | 阿里巴巴集团控股有限公司 | A kind of method and apparatus of recommendation network object |
| CN106951572A (en) * | 2017-04-12 | 2017-07-14 | 上海斐讯数据通信技术有限公司 | A kind of food and drink commending system and dining recommending method |
| CN107180269A (en) * | 2017-05-27 | 2017-09-19 | 维沃移动通信有限公司 | Recommend method and terminal device in a kind of seat |
| CN107562798A (en) * | 2017-08-02 | 2018-01-09 | 北京大学（天津滨海）新代信息技术研究院 | A kind of real-time information recommendation method of the time geodata based on user |
| CN109558961A (en) * | 2017-09-25 | 2019-04-02 | 阿里巴巴集团控股有限公司 | Determine method and system, storage medium, processor and the device of location information |
| CN109688178A (en) * | 2017-10-19 | 2019-04-26 | 阿里巴巴集团控股有限公司 | Recommended method, device and equipment |
| CN109688178B (en) * | 2017-10-19 | 2022-03-11 | 阿里巴巴集团控股有限公司 | Recommendation method, device and equipment |
| CN108009877A (en) * | 2017-11-24 | 2018-05-08 | 阿里巴巴集团控股有限公司 | Information mining method and device |
| CN108460101A (en) * | 2018-02-05 | 2018-08-28 | 山东师范大学 | Point of interest of the facing position social networks based on geographical location regularization recommends method |
| CN108958247A (en) * | 2018-07-02 | 2018-12-07 | 深圳市益鑫智能科技有限公司 | A kind of guided robot |
| CN109902222A (en) * | 2018-11-30 | 2019-06-18 | 华为技术有限公司 | Recommendation method and device |
| US11586941B2 (en) | 2018-11-30 | 2023-02-21 | Huawei Technologies Co., Ltd. | Recommendation method and apparatus |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105718576B (en) | 2019-07-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105718576B (en) | Personalized location recommender system relevant to geographical feature | |
| Jiang et al. | Author topic model-based collaborative filtering for personalized POI recommendations | |
| US11055325B2 (en) | System and method for context enhanced mapping | |
| CN109074358B (en) | Providing geographic locations related to user interests | |
| Jiang et al. | Personalized travel sequence recommendation on multi-source big social media | |
| Zheng et al. | Collaborative location and activity recommendations with gps history data | |
| Lee et al. | Exploration of geo-tagged photos through data mining approaches | |
| CN104123398B (en) | A kind of information-pushing method and device | |
| Zheng et al. | Recommending friends and locations based on individual location history | |
| CN106997389B (en) | Scenic spot recommendation method based on multi-dataset and collaborative tensor decomposition | |
| CN107291888B (en) | Machine learning statistical model-based living recommendation system method near living hotel | |
| US20150112963A1 (en) | Time and location based information search and discovery | |
| KR101213868B1 (en) | Virtual earth | |
| KR101213857B1 (en) | Virtual earth | |
| Xu et al. | A dynamic topic model and matrix factorization-based travel recommendation method exploiting ubiquitous data | |
| Peng et al. | Perceiving Beijing’s “city image” across different groups based on geotagged social media data | |
| CN107341261B (en) | Interest point recommendation method oriented to location social network | |
| CN102067631A (en) | System and method for determination and display of personalized distance | |
| CN105894089A (en) | Method of establishing credit investigation model, credit investigation determination method and the corresponding apparatus thereof | |
| Waga et al. | Recommendation of points of interest from user generated data collection | |
| US20170039264A1 (en) | Area modeling by geographic photo label analysis | |
| Liu et al. | Where your photo is taken: Geolocation prediction for social images | |
| Fränti et al. | Four Aspects of Relevance in Sharing Location-based Media: Content, Time, Location and Network. | |
| CN104994484A (en) | Personal information collection and push system and method based on time and geographic position | |
| Fang et al. | How to extract seasonal features of sightseeing spots from Twitter and Wikipedia (preliminary version) |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |