Social Networks as Data Source for Recommendation Systems

Bank, Mathias; Franke, Juergen

doi:10.1007/978-3-642-15208-5_5

Cited by 3 publications

(3 citation statements)

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“…There are two main sources for traditional recommender systems , which are the free-text-fields and the ratings [59]. Comments are used in e-commerce websites to increase the revenue [60], [61], they allows users to get the experience of other users with a certain product [62].…”

Section: User Content-based As Recommendation Sourcementioning

confidence: 99%

“…But those customers reviews are not accurate as a study showed that the ratings are either of extremely high or extremely low [63]. For that some studies proposed to use social networks as a data source [59]. They used different text mining techniques, as well as web logs and trustful social networks for allowing the customers to get accurate and satisfaction reviews.…”

Section: User Content-based As Recommendation Sourcementioning

confidence: 99%

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Social Networks and Recommender Systems: A World of Current and Future Synergies

Falahi

Mavridis

Atif

2012

Computational Social Networks

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Section: User Content-based As Recommendation Sourcementioning

confidence: 99%

Section: User Content-based As Recommendation Sourcementioning

confidence: 99%

Social Networks and Recommender Systems: A World of Current and Future Synergies

Falahi

Mavridis

Atif

2012

Computational Social Networks

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“…All authors read and approved the final manuscript. 1 School of Computer Engineering and Technology, Shanghai University, Shanghai, China.…”

mentioning

confidence: 99%

Personalized fitting recommendation based on support vector regression

Yao

et al. 2015

Hum. Cent. Comput. Inf. Sci.

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Collaborative filtering (CF) is a popular method for the personalized recommendation. Almost all of the existing CF methods rely only on the rating data while ignoring some important implicit information in non-rating properties for users and items, which has a significant impact on the preference. In this study, considering that the average rating of users and items has a certain stability, we firstly propose a personalized fitting pattern to predict missing ratings based on the similarity score set, which combines both the user-based and item-based CF. In order to further reduce the prediction error, we use the non-rating attributes, such as a user’s age, gender and occupation, and an item’s release date and price. Moreover, we present the deviation adjustment method based on the support vector regression. Experimental results on MovieLens dataset show that our proposed algorithms can increase the accuracy of recommendation versus the traditional CF.

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