1 / 24

Exploiting Geographical Influence for Collaborative Point-of-Interest Recommendation

Exploiting Geographical Influence for Collaborative Point-of-Interest Recommendation. Mao Ye , Peifeng Yin, Wang-Chien Lee, Dik-Lun Lee Pennsylvania State Univ. and HKUST. SIGIR 11. Outline. Introduction and Motivation Model Experiments & Evaluation Conclusions. Introduction.

caine
Download Presentation

Exploiting Geographical Influence for Collaborative Point-of-Interest Recommendation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Exploiting Geographical Influence for Collaborative Point-of-Interest Recommendation Mao Ye , Peifeng Yin, Wang-Chien Lee, Dik-Lun Lee Pennsylvania State Univ. and HKUST SIGIR 11

  2. Outline • Introduction and Motivation • Model • Experiments & Evaluation • Conclusions

  3. Introduction • Location Based Social Network(LBSNs): Foursquare, Gowalla, Brightkite, Loopt etc. • Allow share tips or experience of Point-of-Interest(POIs) e.g. restaurants, stores, cinema through check-in behaviors

  4. Main Elements in LBSNs

  5. Motivation • Recommend new POIs to users can help them explore new places and know their cities better • In LBSNs, different from other systems, “cyber” connections among users as well as “physical” interactions between users and locations captured in the systems, thus POIs recommendation in LBSNs is promising and interesting • The idea of incorporating the geographical influence between POIs has not been investigated previously

  6. Model • Three important factors: • Geographical influence • User preference of POIs • Social Influence • A fusion framework combine all three

  7. Geographical Influence • measures how likely two of a user’s check-in POIs within a given distance • User power law distribution to model the check-in probability to the distance between two POIs visited by the same users: • Given user i and his check-in history Li, then

  8. Geographical Influence • Then for a new location lj, we have the probability for user I to check in ljas follows:

  9. User-based CF • Based on user similarity is the predicted check-in probability. is the similarity of user i and user k, and computed as follows:

  10. Friend Based CF • Based on recommendation from friends • Friends have closer social tie • Friends show more similar check-in bahavior

  11. Fusion Framework • Combine all of the three factors

  12. Data Set

  13. Performance Metrics • Mark off some POIs and the systems return top-N recommended POIs • Mainly examine below two metrics • The ratio of recovered POIs to N, precision@N • The ration of recovered POIs to the total POIs which are marked off , recall@N

  14. Experiments • Model in this paper denotes as USG • U for user preference • S for social influence • G for geographical influence • Compared Methods • User-based CF (U) : set α=β=0 • Friend-based CF (S): set α = 1, β=0 • GI-based (G): set α = 0, β=1 • Random Walk with Restart(RWR) • User preference/social influence based (US): set β=0 • User preference/geographical influence based(UG): set α = 0

  15. Tuning Parameters • User preference plays a dominate role in contributing to the optimal recommendation • Both social and geographical influence are innegligible

  16. Performance Comparison Result • USG always the best • RWR may not be suitable for POI recommendation • Social influence and geographical influence can be utilized to perform POI recommendation

  17. Study on Item-based CF • Regard POIs as “items” and denotes as L , and combine it with user preference(U) and geographical influence(G) • L brings no advantage at all in enhancing U or L in POI recommendation • POIs in LBSNs not have been visited by sufficient users

  18. Study on Social Influence • User check-in behaviors and the user similarity calculated based on RWR • Check-in behaviors and social tie strength • The similarity in friends’ check-in behaviors not necessarily be reflected through social tie strength

  19. Impact of Data Sparsity • The larger the mark-off ratio x is , the sparser the user-Check-in matrix is • Geographical plays an extremely important role when data is very sparse.

  20. Test for Cold Start Users • Consider users who have less than 5 check-ins after mark off 30% • For cold start users, user preference is hard to capture, thus U performs bad , and as few check-ins, G also affects, and S is more useful in this situation

  21. Conclusions • First incorporate geographical influence into POI recommendation • Incorporate U,S,G into a fusion framework • Experiments conclusions • Geographical influence shows a more significant impact than social influence • RWR may be not suitable for POI recommendation, friends’ taste is different( friends have low common check-in ratio) • Item-based CF is not effective

  22. Future Work • Combine semantic tags , e.g. location categories such as Store, Restaurants • Combine geographical influence into Matrix Factorization Method • Take location transition sequence into consideration

  23. Thanks Q&A

More Related