LCARS: A Location-Content-Aware Recommender System

1. LCARS: A Location-Content-Aware Recommender System 資工所  陳冠斌  P76024407 資工所  陳吉德  P76024114 資工所  陳昱琦  P76024295 醫資所  蔡有容  Q56021016 SIGKDD’13 Hongzhi Yin、Bin Cui、Zhiting HuPeking University, Beijing, China Yizhou SunNortheastern University, Boston, USA Ling ChenUniversity of Technology, Sydney, Sydney, Australia

2. Outline • Introduction • Lacation-Content-Aware recommender system • Offline modeling • Online recommendation • Experiments • Settings&Comparative approaches • ResultsRelated work • Conclusion

3. Introduction

4. Introduction (cont’d) • Observation the travel mode of a user • only visit a limitednumber of physical venues User-item sparse • New place: not have any activity history Cold Start • To solve these problems, we add • Spatial item’s location • Content information (e.g. item tags or category words)

5. Introduction (cont’d) • Problem Definition • Given a querying useru with a querying citylu, find k interesting spatial items within lu, that match the preference of u

6. Introduction ( cont’d)

7. LCARS-Preliminary

8. LCARS-Offline Modeling LCA probabilistic mixture generative model the content information of spatial itemin LCA-LDA

9. LCARS-Offline Modeling ( cont’d) We assume that items and their content words are independently conditioned on the topics.

10. LCARS-Offline Modeling ( cont’d) to estimate unknown parameters { θ, θ’, φ, φ’, λ} in the LCA-LDA

11. LCARS-Online recommendation Weight score Offline scoring • Online recommendation part computes a ranking score

12. LCARS-Online recommendation( cont’d) Compute Threshold Algorithm

13. LCARS-Online recommendation( cont’d) Treshold-Based Algorithm

14. Experiments-Datasets • EBSN-DoubanEvent • LBSN-Foursquare

15. Experiments-Comparative approaches User interest, social and geographical influences ( USG) Category-based k-Nearest Neighbors Algorithm ( CKNN) Item-based k-Nearest Neighbors Algorithm ( IKNN) LDA Location-Aware LDA ( LA-LDA) Content-Aware LDA ( CA-LDA)

16. Experiments-Evaluation methods • 1st: • Test set => all spatial items visited by the user in a non-home city • Training set => the rest of user’s activity history in other cities • 2nd: • Test set => 20% of spatial item visited by the user in personal home city • Training set => the rest of personal activity history

17. Experiments-Results_Effectiveness 0.42 0.33 Top-k Performance on DoubanEvent

18. Experiments-Results_Effectiveness ( cont’d) Top-k Performance on Foursquare

19. Experiments-Results_Effectiveness( cont’d) Impact of the Number of Latent Topics

20. Experiments-Results_Efficiency ( cont’d) Efficiency w.r.t Recommendations

21. Conclusion • Facilitates people’s travel • Not only in their home area but also in a new city where they have no activity history • Takes advantage of both the content and location information of spatial items • Overcomes the data sparsity problem in the original user-item matrix

22. Discussion • Will the results be different if they use other evaluated methods? (Because this paper just use recall@k to evaluate the effectiveness) • if they use other methods to evaluate the recommendation system, the results may not be as good as they used recall@k

23. Discussion( cont’d) • As prof. Tseng ask, where is the difference of dataset between figure 3(a)(b)? • Figure 3(a) is the result of the 1st method(ppt page p.16) that divide dataset into testing set and training set

24. Discussion( cont’d) • How’s the results between figure 3(a) and figure 3(b)? Is figure 3(b) better than (a) just because (b)denotes users traveling in home cities? • We can find out the result that (b) is better than (a). However, the distribution of dataset of the two experiments are different. They use different training set to train the model, so there is no basis for comparison

25. Discussion( cont’d) better

26. Discussion( cont’d) • What will we do if we want to compare the difference between querying new cities and querying home cities? • We will use the same dataset to train the offline model and estimate the parameters

27. Thank you for your listening