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FeatureMF: an item feature enriched matrix factorization model for item recommendation

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dc.contributor.author Zhang, Haiyang
dc.contributor.author Ganchev, Ivan
dc.contributor.author Nikolov, Nikola S.
dc.contributor.author Ji, Zhanlin
dc.contributor.author Ó'Droma, Máirtín
dc.date.accessioned 2021-07-07T09:10:30Z
dc.date.available 2021-07-07T09:10:30Z
dc.date.issued 2021
dc.identifier.uri http://hdl.handle.net/10344/10315
dc.description peer-reviewed en_US
dc.description.abstract Matrix Factorization (MF) is one of the most successful Collaborative Filtering (CF) techniques used in recommender systems due to its effectiveness and ability to deal with very large user-item rating matrix. However, when the rating matrix sparseness increases its performance deteriorates. Expanding MF to include side-information of users and items has been shown by many researchers both to improve general recommendation performance and to help alleviate the data-sparsity and cold-start issues in CF. In regard to item feature side-information, most schemes incorporate this information through a two stage process: intermediate results (e.g., on item similarity) are first computed based on item attributes; these are then combined with MF. In this paper, focussing on item side-information, we propose a model that directly incorporates item features into the MF framework in a single step process. The model, which we name FeatureMF, does this by projecting every available attribute datum in each of the item features into the same latent factor space with users and items, thereby in effect enriching item representation in MF. Results are presented of comparative performance experiments of the model against three state-of-the-art item information enriched models, as well as against four reference benchmark models, using two public real-world datasets, Douban and Yelp, with four training:test ratio scenarios each. It is shown to yield the best recommendation performance over all these models across all contexts including data-sparsity situations, in particular, achieving over 0.9% to over 6.5% MAE recommendation performance improvement over the next best model, HERec. FeatureMF is also found to alleviate cold start and to scale well, almost linearly, in regard to computational time, as a function of dataset size. en_US
dc.language.iso eng en_US
dc.publisher IEEE Computer Society en_US
dc.relation.ispartofseries IEEE Access;9, 65266-65276
dc.subject collaborative filtering en_US
dc.subject matrix factorization en_US
dc.subject item features en_US
dc.subject cold start en_US
dc.subject data sparsity en_US
dc.title FeatureMF: an item feature enriched matrix factorization model for item recommendation en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.identifier.doi 10.1109/ACCESS.2021.3074365
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US


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