A METHOD AND A SYSTEM FOR GENERATING CONTEXT-BASED CONTENT RECOMMENDATIONS TO USERS

• US 20150294221A1
• Filed: 07/10/2013
• Published: 10/15/2015
• Est. Priority Date: 07/13/2012
• Status: Abandoned Application

• Current Assignee: Telefonica SA
• Original Assignee: Telefonica SA
• Inventors: ANDRES GUTIERREZ, Juan Jose, VILLEGAS NUNEZ, Paulo, MARTIN MARTINEZ, Manuel, SIERRA MERINO, Javier, RODRIGUEZ PEREZ, Francisco Jose
• Application Number:
  US14/414,588
• Publication Number:
  US 20150294221A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01S 5/0289 of multiple transceivers, e.g. in ad hoc networks
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0261 based on user location
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: H04W 4/021 Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• CPC Class Codes: H04W 4/21 for social networking applications
• CPC Class Codes: H04W 8/005 Discovery of network devices, e.g. terminals

Abstract

A method and a system for generating content recommendations to users.

The method comprising:

• a first user connected through a mobile device to a server, and determining a physical location of said first user mobile device, the method further comprising:
  • a) performing a frequency scan, said first user mobile device, to discover the presence of a plurality of other users'"'"' mobile devices in proximity;
  • b) obtaining, said first user mobile device, a network identifier for each one of said plurality of mobile devices discovered;
  • c) gathering and sending each one of said network identifiers to said server;
  • d) matching, said server, network identifiers received with other users identifiers known by said server,
  • e) predicting affinity between said first user and other based on an spatio-temporal clustering analysis
  • f) storing and analyzing, said server, said affinity prediction in order to provide said content recommendations upon said first user mobile device request

The system of the invention is arranged to implement the method of the invention

Claims

1. A method for generating context-based content recommendations to users, wherein a first user is connected through a mobile device to a server, comprising determining a physical location of said first user mobile device, the method further comprising:

a) performing a frequency scan, said first user mobile device, to discover the presence of a plurality of other users'"'"' mobile devices in proximity;

b) obtaining, said first user mobile device, a network identifier for each one of said plurality of mobile devices discovered;

c) gathering and sending each one of said network identifiers to said server;

d) matching, said server, said network identifiers received with other user identifiers known by said server,

e) predicting affinity between said first user and other based on an spatio-temporal clustering analysis, and

f) storing and analyzing said affinity prediction, said server, in order to provide said content recommendations upon said first user mobile device request.

2. A method according to claim 1, characterized in that obtaining said network identifier comprises obtaining a MAC address of each one of said plurality of mobile devices.

3. A method according to claim 1, characterized in that it comprises performing said affinity prediction in said step e) based on information regarding other users whose mobile devices are in proximity at the same time of said first user mobile device request.

4. A method according to claim 1, characterized in that it comprises performing said affinity prediction in said step e) based on spatio-temporal information regarding said other users whose mobile devices were in proximity at a previous time of said first user mobile device request.

5. A method according to claim 1, characterized in that it comprises performing said affinity prediction in said step e) based on spatio-temporal information regarding said other users whose mobile devices were active in previous occasions and at the same time of said first user former mobile device requests.

6. A method according to claim 1, characterized in that it comprises performing said affinity prediction in said step e) based on spatio-temporal information regarding said other users whose mobile devices were active in previous occasions and at equivalent time periods, said equivalent time periods taken from a predefined set of recurring periodic time intervals of said first user former mobile device requests.

7. A method according to claim 1, characterized in that it comprises performing said affinity prediction in said step e) based on combining the spatio-temporal information regarding said other users as specified by:

information regarding other users whose mobile devices are in proximity at the same time of said first user mobile device request;

spatio-temporal information regarding said other users whose mobile devices were in proximity at a previous time of said first user mobile device request;

spatio-temporal information regarding said other users whose mobile devices were active in previous occasions and at the same time of said first user former mobile device requests; and

spatio-temporal information regarding said other users whose mobile devices were active in previous occasions and at equivalent time periods, said equivalent time periods taken from a predefined set of recurring periodic time intervals of said first user former mobile device requests.

8. A method according to claim 1, characterized in that it comprises computing the spatio-temporal affinity measure between said first user mobile device and each one of said plurality of mobile devices in order to provide said content recommendations.

9. A method according to claim 8, further comprising computing said spatio-temporal affinity measure considering activity of the users in online spaces.

10. A method according to claim 9, characterized in that said online spaces are:

a group in a social network, a discussion group, a blog, among others.

11. A method according to claim 8, characterized in that the final affinity between two users is as a combination of said spatio-temporal affinity and a measure of the similarity of the two profiles of said two users, when said similarity metric is a cosine distance in a vector space model for user features, a neighborhood metric for the matrix of preferences in collaborative filtering, a distance metric extracted from a social graph, among others.

12. A method according to claim 11, in which said combination is a linear combination of said spatio-temporal affinity with said user profile similarity, with adjustable weights providing the level or relative importance for said two operands.

13. A method according to claim 1, characterized in that said content recommendation comprises:

recommending a product, a service and/or a content item to said first user mobile device;

giving an explanation about why said first user mobile device would like said product and/or service content recommendation;

explaining said affinity to said first user mobile device;

giving information about said online spaces and information related to said physical proximity location.

16. A system comprising a server and a user'"'"'s mobile device adapted to implement the method of claim 1.

14. A system for generating context-based content recommendations to users, said system comprising a server and a user'"'"'s mobile device, characterized in that:

said user'"'"'s mobile device, being configured to receive said content recommendation and arranged for;

perform a frequency scan to discover the presence of a plurality of mobile devices in proximity;

obtain a network identifier for each one of said plurality of mobile devices discovered, and

gather and send each one of said network identifier to said server, and

said server arranged to;

match the network identifiers received with other user identifiers known by said server,

predict affinity between said first user and other based on an spatio-temporal clustering analysis, and

store and analyze, said matching in order to provide said content recommendations upon said user'"'"'s mobile device request.

15. A system according to claim 14, characterized in that said server further comprises:

a location module arranged to perform an analysis of the said proximity data;

an affinity module arranged to perform an analysis between said user'"'"'s mobile device and each one of said plurality of mobile devices;

a cluster module arranged to create groups of clusters of similar nearby users;

a recommender engine to provide said content recommendation to said user'"'"'s mobile device from each one of said plurality of mobile devices belonging to the same group of clusters;

a social network analyzer module arranged to analyze online activity of said users;

a sources manager module in charge of manage all of said online activity; and

a SN activity collector module arrange to gather all the online activity information analyzed.