Cited By
View all- Ali WKumar JShao J(2022)Inferring context with reliable collaborators: a novel similarity estimation method for recommender systemsApplied Intelligence10.1007/s10489-022-03162-752:12(13883-13901)Online publication date: 1-Sep-2022
Recommending content using side information
Pages 3353 - 3374
Abstract
Collaborative Filtering methods predict user interests and make recommendations just by using the rating matrix. However, in practice there is extensive side information about users and items, such as the age of the user, the actors in a movie, or the abstract of a journal article. In this paper, a novel model called Collaborative Poisson Factorization with Side-information (CPFS) is proposed which extends CTPF by incorporating richer kinds of side information conditionally as a prior to the model. CPFS is a monolithic hybridization model that combines features from different data sources into a single recommendation algorithm. We develop a Gibbs sampler and also a Variational method with closed-form updates for the inference of CPFS and demonstrate its applicability on a range of datasets including movies, books, academic papers, and travel. The extension improves prediction quality, especially in the cold start scenario. The connections between side information and topics are also intuitive.
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Index Terms
- Recommending content using side information
Index terms have been assigned to the content through auto-classification.
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© Springer Science+Business Media, LLC, part of Springer Nature 2020.
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Kluwer Academic Publishers
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Publication History
Published: 01 June 2021
Accepted: 12 September 2020
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View all- Ali WKumar JShao J(2022)Inferring context with reliable collaborators: a novel similarity estimation method for recommender systemsApplied Intelligence10.1007/s10489-022-03162-752:12(13883-13901)Online publication date: 1-Sep-2022