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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.

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Exploiting Geographical Influence for Collaborative Point-of-Interest Recommendation

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  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

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