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Based on the intuition that users’ social friend information can help improve the prediction accuracy of recommender systems, we propose two social recommendation algorithms that impose social regularization terms to constrain matrix factorization objective functions
Recommender systems with social regularization
WSDM, pp.287-296, (2011)
Although Recommender Systems have been comprehensively analyzed in the past decade, the study of social-based recommender systems just started. In this paper, aiming at providing a general method for improving recommender systems by incorporating social network information, we propose a matrix factorization framework with social regulariz...More
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- As an indispensable type of Information Filtering technique, recommender systems have attracted a lot of attention in the past decade.
- Related recommendation techniques have been widely studied in research communities of information retrieval [5, 8, 12, 21, 36, 38, 39, 40], machine learning [28, 30, 31, 33, 41] and data mining [1, 3, 11, 14, 15]
- Due to their great commercial value, recommender systems have been successfully deployed in industry, such as product recommendation at Amazon, music recommendation at iTunes, movie recommendation at Netflix, etc.
- In order to improve recommender systems and to provide more personalized recommendation results, the authors need to incorporate social network information among users
- As an indispensable type of Information Filtering technique, recommender systems have attracted a lot of attention in the past decade
- The problem we study in this paper is different from traditional recommender systems since the latter normally only considers the user-item rating matrix
- We propose our first social recommendation model based on matrix factorization technique, min L1(R, U, V )
- 13.07 into our social regularization framework to further improve the performance of recommender systems
- We focus on the social recommendation problem which is rarely studied in the literature before
- Based on the intuition that users’ social friend information can help improve the prediction accuracy of recommender systems, we propose two social recommendation algorithms that impose social regularization terms to constrain matrix factorization objective functions
- The empirical analysis on two large datasets demonstrates that the approaches outperform other state-of-the-art methods.
- In this paper, aiming at solving the problems mentioned above, the authors propose two social recommendation methods that utilize social information to improve the prediction accuracy of traditional recommender systems.
- The experimental analysis on two large datasets shows that the methods outperform other state-of-the-art algorithms.
- 13.07 into the social regularization framework to further improve the performance of recommender systems
- CONCLUSION AND FUTURE
In this paper, the authors focus on the social recommendation problem which is rarely studied in the literature before.
- In order to model the social recommender systems more realistically, in the future, the authors need to design an effective algorithm to identify the most suitable group of friends for different recommendation tasks.
- This is a research direction worthy of further exploration, and it may need to develop a scalable and effective user clustering method
- Table1: Statistics of User-Item Matrix of Douban
- Table2: Statistics of Friend Network of Douban
- Table3: Statistics of User-Item Matrix of Epinions
- Table4: Statistics of Trust Network of Epinions
- Table5: Performance Comparisons (Dimensionality = 10)
- Table6: Similarity Analysis (Dimensionality = 10)
- In this section, we review several major approaches to recommender systems, including (1) traditional recommender systems which are mainly based on collaborative filtering techniques, (2) trust-aware recommender systems which have drawn lots of attention recently, and (3) social recommender systems which we study in this paper.
2.1 Traditional Recommender Systems
As mentioned in , one of the most commonly-used and successfully-deployed recommendation approaches is collaborative filtering. In the field of collaborative filtering, two types of methods are widely studied: neighborhood-based approaches and model-based approaches. Neighborhoodbased methods mainly focus on finding the similar users [4, 12] or items [6, 17, 32] for recommendations. User-based approaches predict the ratings of active users based on the ratings of similar users found, while item-based approaches predict the ratings of active users based on the computed information of items similar to those chosen by the active user. User-based and item-based approaches often use Pearson Correlation Coefficient (PCC) algorithm  and Vector Space Similarity (VSS) algorithm  as the similarity computation methods. PCC method can generally achieve higher performance than VSS approach, since the former considers the differences of user rating style.
- The work described in this paper was fully supported by two grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No CUHK4154/10E and CUHK 4152/10E) and a Google Focused Grant Project under “Mobile 2014”
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