Location Recommendation for Location-based Social Networks

1. Location Recommendation for Location-based Social Networks By Mao Ye, Peifeng Yin and Wang-Chien Lee Presenter: Naveenkumar Selvaraj

2. Limitations of Previous Work The previous techniques for online-location recommendations : Incur high communication overhead Are not efficient owing to the rapidly growing number of users and locations and location based social network

3. Essence of the Paper Proposes techniques which Explores the strong social and geospatial ties among users and their favorite locations in the system Have comparable recommendation effectiveness against state-of-the-art recommendation algorithms Incur significantly low computational overhead, and thereby improved efficiency compared to other recommendation algorithms

4. Location based Social Network- Foursquare Mobile Web Application Available to users of iPhone, Android and Blackberries Enables update of locations Know where your friends are Check-in Mayorship Rate the location

5. Techniques for filtering The paper proposes two filtering techniques: Friend-based Collaborative Filtering (FCF) Geo-measured Friend-based Collaborative Filtering (GM-FCF)

6. Spatio-Social Analysis Data Collection Crawl live data from Four-Square Select a well-connected user and we start crawling When a user is visited, we extract the address the list of friends The locations where he/she visited Through a BST, we obtain user data set By crawling locations collected in user data collection process, the location addresses and list of visitors are extracted

7. Spatio-Social Analysis Data Analysis Common Location Ratio Plot the Common Location Ratio for friends and non-friends About 4% of friends have a common Location Ratio >10% Common Location Ratio show common location interests between users Shows Friends share much more common interests than two arbitrary people Friends may play positive role in collaborative recommendation

8. Spatio-Social Analysis Data Analysis (contd..) to find what kind of friends share more commonly visited locations than others find the correlation between common location ratio and distance between friend pairs group friends based on their distance in group of 10 m From the graph- evident that nearby friends have higher probability to share common locations

9. Friend-based Collaborative Filtering Social friends share more common locations than non-friends Only need to compute the similarity weight between friends, instead of all users and the current user. Tradeoff: Since non-friends are not considered, much noise is reduced and Precision is good Non-friends also contribute to recommendation when they share common locations. Eliminating them may affect Recall In FCF, for a given user only his friends are involved in the computation and contribute to missing rating prediction Cost(FCF)<<Cost(CF) , because the number of friends is very less compared to total number of users

10. Geo-measured Friend-Based Collaborative Filtering Nearby friends tend to share more commonly visited locations Instead of scanning friend’s visited locations, model the similarity weight between friends by distance Instead of scanning all locations , access only longitude and longitude for similarity weight estimation

11. Metrics For each individual in the dataset , 20% of all locations he/she visited is removed. Use Recommendation Algorithms to recover missing user-location pairs that are removed Effectiveness: Evaluate how many locations removed in pre-processing step re-appear in recommendation results Consider Precision and Recall of returned results Efficiency Collaborative Filtering similarity calculation and rating prediction- both involve matrix mutliplication Measures the computational cost across the algorithms

12. Experiments and Results FCF considers only friends in recommendation Hence lower recall value But differences in recall amongst all is insignificant Very competitive in comparison with other Collaborative Recommendation Techniques

13. Experiments and Results FCF outperforms other techniques in order of magnitude Because it considers only friends, leading to much lesser computational cost GM-FCF is also competitive in terms of effectiveness and shows great advantages in efficiency

14. Future Research Directions One possible research direction is trying to improve the effectiveness of the search results returned using FCF and GM-FCF using data mining techniques. This can be extended to provide location based news feeds, inform emergency information at a location, etc

15. Conclusion There exist social and geo-spatial ties among users and their visited locations FCF approach for location recommendations based on collaborative filtering GM-FCF, variant of FCF is proposed based on heuristics derived from geo-spatial characteristics in Foursquare Finally, both the techniques are validated and evaluated using comprehensive examination

16. Questions ?