Abstract
Collaborative filtering (CF), the most prevalent technique in the area of recommender systems (RSs), provides suggestions to users based on the tastes of their similar users. However, the new user and sparsity problems, degrade its efficiency of recommendations. Trust can enhance the recommendation quality by mimicking social dictum “friend of a friend will be a friend”. However distrust, the another face of coin is yet to be explored along with trust in the area of RSs. Our work in this paper is an attempt toward introducing trust-distrust enhanced recommendations based on the novel similarity measure that combines user ratings and trust values for generating more quality recommendations. Our approach also exploits distrust links among users and analyses their propagation effects. Further, distrust values are also used for filtering more distrust-worthy neighbours from the neighbourhood set. Our experimental results show that our proposed approaches outperform the traditional CF and existing trust enhanced approaches in terms of various performance measures.
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Chug, S., Kant, V., Jadon, M. (2017). Trust Distrust Enhanced Recommendations Using an Effective Similarity Measure. In: Ghosh, A., Pal, R., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2017. Lecture Notes in Computer Science(), vol 10682. Springer, Cham. https://doi.org/10.1007/978-3-319-71928-3_7
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DOI: https://doi.org/10.1007/978-3-319-71928-3_7
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