Social Recommendation a Review.pptx
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Social Recommendation A Review ACM Transactions on Intelligent Systems and Technology 2013 Jiliang Tang · Xia Hu · Huan Liu Associate professor at Michigan.
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Social Recommendation a Review.pptx
- 1. Presented by : Habiba Abderrahim Dhia Elhak Bouslimi
- 2. Social Recommendation A Review ACM Transactions on Intelligent Systems and Technology 2013 Jiliang Tang · Xia Hu · Huan Liu Associate professor at Michigan State University in the Computer Science and Engineering Department
- 3. OUTLINE RECOMMENDATION SYSTEM TRADITIONAL RS Vs SRS Existing SRS Evaluation Metrics Summary POSITIVE AND NEGATIVE SR Classifications INTRODUCTION Why Recommendation ? 01 02 04 Experience SOCIAL RECOMMENDER SYSTEM 03 CONCLUSION 06 3 Future work Research directions 05
- 4. INTRODUCTION 01 4
- 5. ? Recommender System SR 5
- 6. 6 Recommendation system Collaborative filtering (CF) Hybrid Memory Based Model Based Combining different recommenders Adding CF based characteristics to content based models Content Based user-oriented methods item-oriented methods Pearson Correlation Coefficient Cosine similarity Probability-based similarity Computing similarity for user-oriented methods Adding content based characteristics to CF user-item matrix or a sample to generate a prediction ? 5 1 4 4 1.5 Bayesian Bayesian belief net Factorization based CF Bayesian vector of weights TF-IDF Suggest items with similar keywords
- 7. Social Recommender system SRS 02 7
- 8. Broad Definition 8 Narrow Definition Social recommendation systems are systems that make recommendations to users based on social information, such as social connections, relationships, preferences and and behavior . This definition encompasses systems that use only social information to make recommendations and do not use other types of information, such as individual preferences and behavior. conjunction with other types of information only social information
- 9. 9 Users’ preferences are likely to be similar to or influenced by their connected friends Social relation users are correlated when they establish social relations Trust influence homophily
- 10. 10 66% of people on social sites have asked friends or followers to help them make a decision . 88% of links that 14-24 year olds clicked were sent to them by a friend . 78% of consumers trust peer recommendations over ads and Google SERPs.
- 11. what make social recommendation different from Traditional recommender 11 ?
- 12. 12 Rating Matrix Traditional Recommender System Makes recommendations based on the behavior of similar users
- 13. 13 Social Recommender System Rating Matrix Tij = 1 if uj connects to ui users can connect to each other a basic CF model + a social information model
- 14. Existing Social Recommender Systems 14 Social Recommender Systems Memory based TidalTrus t TrustWalker Model based Co- factorizatio n Regulariz ation TidalTrust : method aims to make recommendations based not only on user preferences and item characteristics, but also on the trustworthiness of users and items. TidalTrust evaluates trustworthiness TrustWalker : Uses a random walk technique to find the most trustworthy individuals in a social network and make recommendations based on their preferences Co-factorization: is a technique used in recommendation systems to combine multiple matrices into a single factorized representation. The goal is to improve the accuracy of recommendations. Regularization: is a technique used in machine learning to prevent overfitting by adding a penalty term to the objective function being optimized.
- 15. Existing Social Recommender Systems 15 Social Recommender Systems Memory based TidalTrus t TrustWalker Model based Co- factorizatio n Regulariz ation Co-factorization: is a technique used in recommendation systems to combine multiple matrices into a single factorized representation. The goal is to improve the accuracy of recommendations. Regularization: is a technique used in machine learning to prevent overfitting by adding a penalty term to the objective function being optimized.
- 16. Benchmark 16
- 17. Epinion03 17 This dataset provides user-item rating information and user-user trust networks it contains : ● 49,290 users who rated ● 139,738 different items ● 664,824 reviews and Rating ● 487,181 trust statements. The dataset was collected from the Epinions Web site ratings_data.txt.bz user_id |item_id| rating_value trust_data.txt.bz2 source_user_id |target_user_id | trust_statement_value 22605 | 18420 | 1 https://www.shopping.com/search.html?c=Home%20%26%20Garden~~1_13000000
- 18. 18 Flixster Users can rate movies and can also add some users to their friend list,establishing social networks. Flixster provides user-item rating information and user-user friendship networks. CIAO A product review website where users can rate and write reviews for various products, and they can also establish social relations with others. Extra contextual information + Epinions11 This dataset includes richer information for social recommendation, including temporal information for both rating and social information, categories of products, information about reviews, and distrust information, which allows advanced research about social recommendation.
- 19. 19
- 20. Evaluation Metrics 20
- 21. 21 Prediction accuracy RMSE MAE ● Measures the closeness of predicted ratings to the true ratings ● A smaller RMSE or MAE value means better performance
- 22. 22 Ranking accuracy Precision Recall Ranking accuracy evaluates how many recommended items are purchased by the user.
- 23. Key Finding - Positive and Negative experience 23 Can SRS improve recommendation performance ?
- 24. Key Finding 24 Data sparsity : The rating matrix is usually very sparse due to most users rating few of the millions of items system is forced to choose neighbors in the small portion of comparable users and is probably going to miss other non-comparable but relevant users Cold star : Most of these systems are not able to generate accurate recommendations for users with few or no ratings . Social recommendation can make recommendations as long as the user is connected to a large enough component of the social network . Recommendation can significantly reduce cold-start users.
- 25. BUT … 25 valuable friends Casual friends Users are not necessarily all that similar event friends
- 26. Research directions 26 improve performance of social recommander
- 27. 27 The Heterogeneity of Social Networks People place trust differently to users in different domains
- 28. 28 ● Temporal Information ● Social relations also change over time New social relations are added, while existing social relations become inactive or are deleted. The changes of both ratings and social relations further exacerbate the difficulty of exploiting temporal information for social recommendation. Customer preferences for products drift over time !
- 29. 29 Cross Media Data A user generally has multiple accounts in social media. A new user on one website might have existed on another website for a long time. Integrating networks from multiple websites can bring about a huge impact on social recommender systems and provide an efficient and effective way to solve the cold-start problem.
- 30. Conclusion 05 30
- 31. Thanks! Do you have any questions? 31
Editor's Notes
- brev what’s a review
- We begin by giving formal definitions of social recommendation and discuss the unique property of social recommendation and its implications compared with those of traditional recommender systems.
- A review of the existing research in the field of social recommendation classify existing social recommender systems according to the basic models adopted to build the systems and research directions to improve social recommendation capabilities.
- Memory based methods use either the whole user-item matrix or a sample to generate a prediction [105], which can be further divided into user-oriented methods [44, 10] and item-oriented methods [9 Well-known model-based methods include Bayesian belief net
- Now what are the problem with customer reviews ?
- not limited to online social relations but include all kinds of available social media data such as social tagging [69], user interactions [52] and user click behaviors
- The idea is that users tend to trust and be influenced by the opinions and preferences of their friends, so incorporating information about these social connections can help improve the accuracy of recommendation algorithms. By using both the rating information (e.g. how a user rated certain items) and social information (e.g. who a user is connected to), social recommendation systems aim to capture these social connections and use them to make more personalized recommendations for users.
- , traditional recommender systems assume that users are independent and identically distributed (known as the i.i.d. assumption). However, online users are inherently connected via various types of relations such as friendships and trust relations
- since most of SRS uses CF Following the classification of CF based recommender systems, we classify social recommender systems into two major categories according to their basic CF models: memory based social recommender systems and model based social recommender systems.
- since most of SRS uses CF Following the classification of CF based recommender systems, we classify social recommender systems into two major categories according to their basic CF models: memory based social recommender systems and model based social recommender systems.
- provide the data that is used to train and evaluate recommendation algorithms.
- Epinions Epinions is a website where people can review products. These datasets are very popular in Recommender Systems which can be used as baseline. http://www.trustlet.org/downloaded_epinions.html
- (temporal information about when ratings are provided, the category information of products, and information about the reviews such as the content and helpfulness votes
- Ranking accuracy measures the effectiveness of a recommendation system by comparing the items recommended to a user and the items actually purchased by the user. Precision measures the proportion of recommended items that were purchased, while recall measures the proportion of purchased items that were recommended. F-score is a combined metric that balances precision and recall, and is less affected by the length of the recommendation list.
- key findings from both positive and negative experiences in applying social recommender systems to seek deeper understanding and further development of social recommendation
- Sparse datasets with high zero values can cause problems like over-fitting in the machine learning models and several other problems.
- valuable friends, casual friends and event friends, users are not necessarily all that similar and social relations mixed with useful and noise connections may introduce negative information into recommender systems [92]. For example, social recommender systems simply using all available relations perform worse than traditional recommender systems
- key findings from both positive and negative experiences in applying social recommender systems to seek deeper understanding and further development of social recommendation
- u1’s social relations with {u2, u3, . . . , u9}. The user u1 may treat her social relations differently in different domains. For example, u1 may seek suggestions about “Sports” from {u2, u3}, but ask for recommendation about “Electronics” from {u4, u5}. In [110], the authors found that people place trust differently to users in different domains. For example, ui might trust uj in “Sports” but not trust uj in “Electronics” at all. For different sets of items, exploiting different types of social relations can potentially benefit existing social recommender systems [