1. Chengjie Sun,Lei Lin, Yuan Chen, Bingquan Liu Harbin Institute of Technology School of Computer Science and Technology 1 19/11/2013 12:09 PM

2. Outlines Background and challenges Related works Methodology description Experiments and analyses Conclusion and further works 2

3. Background With the rapid development of the Internet, social network services such as Twitter, Facebook, and Tencent Weibo become more and more popular. Though social network benefits us a lot, it also wastes us too much time to search useful information from massive information. To deal with the problem of choosing information sources efficiently, it is important to develop effective social recommender system to help users easily find the potential items they want to follow. 3

4. Challenges Social recommender system has much more social information to use than traditional recommender system. Social information includes social relationships, user’s profile, user’s social action and so on. How to take advantage of these information to improve the performance of social recommender system is both a good way and a big challenge. 4

5. Solutions To this end, we concentrate on combining social relationships into recommendation algorithm to improve the performance of social recommender system. 5

6. Outlines Background and challenges Related works Methodology description Experiments and analyses Conclusion and further works 6

7. Related works The related works are told from two aspects: The popular methods of recommender system The research of social recommendation 7

8. Methods for recommender system The goal of recommender system is to suggest users with items which may fit the users’ tastes. Recommendation methods can be classified into the three categories: content-based, collaborative filtering (CF), and hybrid recommendation approaches. As one of the most successful methods, CF has been widely used in business and deeply studied in academia. To deal with the data sparsity challenge, many algorithms derived from CF have been proposed, such as Singular Value Decomposition (SVD) and SVD++. 8

9. Researches for social recommendation Social recommendation aims at forming a recommender system by using recommendation methods with information from social networks such as user profile, user action, and social relationship. 9

10. Two recommendation tasks One is KDD Cup 2012 Track1, which is to develop such a system aiming at recommending items (celebrities) which could be persons, organizations, or groups in Tencent Weibo. The other is Artist recommendation in Last.fm online music system, which proposes a novel hierarchical Bayesian model incorporating topic modeling and probabilistic matrix factorization. 10

11. Outlines Background and challenges Related works Methodology description Experiments and analyses Conclusion and further works 11

12. Latent Factor Model We use Latent factor model as our basic method. Latent factor models have been widely used in rating prediction problem. The main idea of latent factor model is to predict a rating by the dot product of a user latent factor and an item latent factor. Singular Value Decomposition (SVD) is one of the most popular latent factor models. 12

13. SVD The basic SVD model with user bias, item bias and global bias can be described in the following equation: Here is the global bias, is the user bias, is the item bias, is the user latent factor and is the item latent factor. 13

14. SGD Stochastic Gradient Descent algorithm (SGD) is an efficient and popular strategy to solve the above optimization problem. The main idea of SGD is to minimize the error by taking a small step on parameters along the direction of gradient descent. 14

15. Incorporate Implicit Feedback The datasets are usually much more complicated for building recommender system in the real world. For example, in a movie recommender system, there are many other kinds of information which may include movie tags, movie category, user profile, user social network and so on. How to utilize these diverse implicit feedback information is beyond the ability of SVD, while SVD++ can do it well by modeling them as latent features. 15

16. SVD++ and implicit information The only thing has to be done is extending the SVD++ to the following form: The adding sum term represents the perspective of implicit feedback. can be user’s neighbors, tags or other implicit feedbacks. is the latent factor and is its weight. 16

17. Benefit User information such as user’s tags, keywords and history following records are very sparse which will lead to the features being ineffective. Using social relationships can deal with the feature sparsity problem to some extent. We can use friends’ tags, keywords and history following records as a kind of expanding features. 17

18. Outlines Background and challenges Related works Methodology description Experiments and analyses Conclusion and further works 18

19. Datasets We conducted our experiments on two real world datasets. The first one is a subset of dataset of Track 1 in KDD Cup 2012 provided by Tecent Weibo. The dataset is a snapshot of Tencent Weibo of five days. Another dataset is hetrec2011-lastfm-2k obtained from Last.fm online music system. There are listening histories, tagging histories and friend relationships in the dataset. 19

20. Statistics on two datasets 20

21. Metrics (Mean Average Precision of Top N) Recall@n The metric are defined as: 21

22. Experiment description For Tencent Weibo dataset, we conducted our experiments on four user features: follow histories, tags, keywords and actions. For Last.fm dataset, we conducted our experiments on the feature of tags. Tag features in Last.fm dataset are tags a user has made for artists. 22

23. Results for Tencent Weibo 23

24. Results for Last.fm dataset 24

25. Analyses of results Previous two tables showed the results of our experiments on Tencent Weibo dataset and Last.fm dataset. We can see that all of the expanding features can achieve improvement when used independently or by combination. Except follow history in Tencent Weibo dataset, other expanding features in both datasets were even more useful than the original features. 25

26. Outlines Background and challenges Related works Methodology description Experiments and analyses Conclusion 26

27. Conclusion We explored a new way of using social relationships by expanding user features in social recommender systems. Experimental results showed that expanding features by social relationships can improve the performance of recommender system efficiently. 27

28. End Thank you! 28