A Comparison of Collaborative Filtering-based Recommender Systems (original) (raw)

2018, Journal of emerging technologies and innovative research

Abstract

The proliferation of Internet has made people to rely on virtual recommendations. Recommender systems help out in giving important recommendations. Collaborative filtering is the most successful and widely used approach in designing recommender systems since the introduction of the concept of recommender systems. This approach uses the known tastes and preferences of a set of users to make predictions or generate recommendations about the unknown tastes and preferences of the target user. This paper discusses various works which use collaborative filtering approach to design recommender systems. The paper also gives a comparison of these approaches. IndexTerms – Recommender Systems, Collaborative filtering, Social networks. ________________________________________________________________________________________________________

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References (19)

  1. Konstas et al. [13] used social information from Last.fm to improve the collaborative filtering approach. The social metadata like tags and friendships of users is used to enhance the capabilities of this approach. Ma et al. [14] propose another collaborative filtering RS called SoRec which is based on trusted social relations. SoRec helps in alleviating the data sparsity problem. Yang et al. [15] emphasized that a user trusts his friends differently based on the context of items. They proposed a circle-based RS in online social networks by using the matrix factorization approach of collaborative filtering. Using this approach, they discover the expert level of their friends for different set of items. The drawback of this approach is that it uses only item category as the context. Ma et al. [16] emphasized that there is difference between trusted relationships and social relations. They highlighted that social fiends may have different tastes. A user having multiple friends on social networks do not mean that he trusts all his social friends equally. They incorporated the social information into RS. They define two social regularization models where the first model is: REFERENCES
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