International Conference on Computer and Knowledge Engineering

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Home / 13th International Conference on Computer and Knowledge Engineering

Link Prediction for Recommendation based on Complex Representation of Items Similarities

Authors :

Masoumeh Alinia¹ Seyed Mohammad Hossein Hasheminejad² Hadi Shakibian³

1- Faculty of Engineering, Alzahra University, Tehran, Iran 2- 3-

Keywords :

Recommender System،Link Prediction،Collaborative Filtering،Coverage،Hit Rate

Abstract :

Recommender systems have different goals, including coverage and hit rate. A graph-based recommender system that includes users' ratings of items can be represented as a bipartite graph, where users and items are nodes and ratings are treated as edges. The recommendation task in bipartite recommender systems can be modeled as a subproblem of link prediction. Modified link prediction methods are used in related research to distinguish between fundamental relational dichotomies like versus dislike, and similar versus dissimilar. However, similarities between users and items should be noticed. This paper introduces a method that simultaneously considers user feedback (positive and negative) and item similarity, focusing on the coverage objective. The results over a real-world dataset indicate the effectiveness of the proposed approach in terms of the coverage as well as the hit rate metrics.

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