FeatureMF: An Item Feature Enriched Matrix Factorization Model for Item Recommendation
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. Expandi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9409095/ |
| _version_ | 1819145838402732032 |
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| author | Haiyang Zhang Ivan Ganchev Nikola S. Nikolov Zhanlin Ji Mairtin O'Droma |
| author_facet | Haiyang Zhang Ivan Ganchev Nikola S. Nikolov Zhanlin Ji Mairtin O'Droma |
| author_sort | Haiyang Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T13:04:23Z |
| format | Article |
| id | doaj.art-41f5ebb7e1a74ebb91442ffbb30c6b2b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T13:04:23Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-41f5ebb7e1a74ebb91442ffbb30c6b2b2022-12-21T18:24:54ZengIEEEIEEE Access2169-35362021-01-019652666527610.1109/ACCESS.2021.30743659409095FeatureMF: An Item Feature Enriched Matrix Factorization Model for Item RecommendationHaiyang Zhang0https://orcid.org/0000-0002-3025-9609Ivan Ganchev1https://orcid.org/0000-0003-0535-7087Nikola S. Nikolov2https://orcid.org/0000-0001-8022-0297Zhanlin Ji3Mairtin O'Droma4Department of Computer Science, The University of Sheffield, Sheffield, U.K.Department of Computer Systems, University of Plovdiv Paisii Hilendarski, Plovdiv, BulgariaTelecommunications Research Centre (TRC), University of Limerick, Limerick, IrelandTelecommunications Research Centre (TRC), University of Limerick, Limerick, IrelandTelecommunications Research Centre (TRC), University of Limerick, Limerick, IrelandMatrix 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.https://ieeexplore.ieee.org/document/9409095/Collaborative filteringmatrix factorizationitem featurescold startdata sparsity |
| spellingShingle | Haiyang Zhang Ivan Ganchev Nikola S. Nikolov Zhanlin Ji Mairtin O'Droma FeatureMF: An Item Feature Enriched Matrix Factorization Model for Item Recommendation IEEE Access Collaborative filtering matrix factorization item features cold start data sparsity |
| title | FeatureMF: An Item Feature Enriched Matrix Factorization Model for Item Recommendation |
| title_full | FeatureMF: An Item Feature Enriched Matrix Factorization Model for Item Recommendation |
| title_fullStr | FeatureMF: An Item Feature Enriched Matrix Factorization Model for Item Recommendation |
| title_full_unstemmed | FeatureMF: An Item Feature Enriched Matrix Factorization Model for Item Recommendation |
| title_short | FeatureMF: An Item Feature Enriched Matrix Factorization Model for Item Recommendation |
| title_sort | featuremf an item feature enriched matrix factorization model for item recommendation |
| topic | Collaborative filtering matrix factorization item features cold start data sparsity |
| url | https://ieeexplore.ieee.org/document/9409095/ |
| work_keys_str_mv | AT haiyangzhang featuremfanitemfeatureenrichedmatrixfactorizationmodelforitemrecommendation AT ivanganchev featuremfanitemfeatureenrichedmatrixfactorizationmodelforitemrecommendation AT nikolasnikolov featuremfanitemfeatureenrichedmatrixfactorizationmodelforitemrecommendation AT zhanlinji featuremfanitemfeatureenrichedmatrixfactorizationmodelforitemrecommendation AT mairtinodroma featuremfanitemfeatureenrichedmatrixfactorizationmodelforitemrecommendation |