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• W3157728665 endingPage "115108" @default.
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• W3157728665 abstract "Graph-based recommender systems (GRSs) analyze the structural information available in the graphical representation of data to make better recommendations, especially when direct user-item relation data is sparse. Ranking-oriented GRSs mostly use graphical representation of preference (or rank) data for measuring node similarities, from which they can infer recommendations using neighborhood-based methods. In this paper, we propose PGRec, a novel model-based ranking-oriented recommendation framework. Unlike many other graph-based methods, PGRec extracts vector representations for users and preferences from a novel graph structure called PrefGraph, which models entity relations, feedbacks, and content. A general graph-embedding process is improved and applied to extract vector representations for entities. The resulting embeddings are then used for predicting the target user’s unknown pairwise preferences by a neural network based on which a recommendation list is generated for the target user. We have evaluated the proposed method’s performance against the state of the art model-based and neighborhood-based recommendation algorithms. Our experiments show that PGRec outperforms the baseline algorithms in terms of the NDCG metric in several datasets." @default.
• W3157728665 created "2021-05-10" @default.
• W3157728665 creator A5029123695 @default.
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• W3157728665 date "2021-11-01" @default.
• W3157728665 modified "2023-09-26" @default.
• W3157728665 title "Embedding ranking-oriented recommender system graphs" @default.
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• W3157728665 doi "https://doi.org/10.1016/j.eswa.2021.115108" @default.
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