#5906. A hybrid multi-criteria collaborative filtering model for effective personalized recommendations

August 2026publication date
Proposal available till 03-06-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Geometry and Topology;
Theoretical Computer Science;
Software;
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Abstract:
Recommender systems act as decision support systems in supporting users in selecting the right choice of items or services from a high number of choices in an overloaded search space. However, such systems have difficulty dealing with sparse rating data. 0ne way to deal with this issue is to incorporate additional explicit information, also known as side information, to the rating information. However, this side information requires some explicit action from the users and often not always available. Accordingly, this study presents a hybrid multi-criteria collaborative filtering model. The proposed model exploits the mul­ti-criteria ratings, implicit similarity, similarity transitivity and global reputation concepts to expand the space of potential recommenders. This expansion will enhance the prediction accuracy and coverage of the proposed model when applied to sparse data situations.
Keywords:
Collaborative filtering; Global reputation; Implicit similarity; Multi-criteria; Recommender systems; References

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