Improving Collaborative Filtering Algorithms: Sentiment-based Approach in Social Network

Improving Collaborative Filtering Algorithms: Sentiment-based Approach in Social Network

Firas Ben Kharrat, Aymen Elkhleifi, Rim Faiz
DOI: 10.4018/978-1-7998-0951-7.ch031
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This paper puts forward a new recommendation algorithm based on semantic analysis as well as new measurements. Like Facebook, Social network is considered as one of the most well-prominent Web 2.0 applications and relevant services elaborating into functional ways for sharing opinions. Thereupon, social network web sites have since become valuable data sources for opinion mining. This paper proposes to introduce an external resource a sentiment from comments posted by users in order to anticipate recommendation and also to lessen the cold-start problem. The originality of the suggested approach means that posts are not merely characterized by an opinion score, but receive an opinion grade notion in the post instead. In general, the authors' approach has been implemented with Java and Lenskit framework. The study resulted in two real data sets, namely MovieLens and TripAdvisor, in which the authors have shown positive results. They compared their algorithm to SVD and Slope One algorithms. They have fulfilled an amelioration of 10% in precision and recall along with an improvement of 12% in RMSE and nDCG.
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According to Burke (Burke, 2002), Recommender systems have three fundamental categories: collaborative-filtering, content-based and hybrid. The hybrid recommender system combines the content-based and the collaborative-filtering regarding the kind of advocated data Recommendation types are explained in details with their limitations in this section:

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