Item-Based Collaborative Memory Networks for Recommendation
Item-based Collaborative Filtering (ICF or Item-based CF) has been widely applied for recommender systems in industrial scenarios, owing to its efficiency in user preference modeling and flexibility in online personalization. It captures a user's preference from his or her historically interact...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9264159/ |
| _version_ | 1831558609921638400 |
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| author | Dewen Seng Guangsen Chen Qiyan Zhang |
| author_facet | Dewen Seng Guangsen Chen Qiyan Zhang |
| author_sort | Dewen Seng |
| collection | DOAJ |
| description | Item-based Collaborative Filtering (ICF or Item-based CF) has been widely applied for recommender systems in industrial scenarios, owing to its efficiency in user preference modeling and flexibility in online personalization. It captures a user's preference from his or her historically interacted items, recommending new items that are the most similar to the user's preference. Recently, several works propose advanced neural network architectures to learn item similarities from rating data, by modeling items as latent vectors and learning model parameters by optimizing a recommendation-aware objective function. While much literature attempts to use classical neural networks such as multi-layer perception (MLP), to learn item similarities, there has been relatively less work employing memory networks for ICF, which can more accurately record the detailed information about entities than classical neural networks. Therefore, in this paper, we propose a powerful Item-based Collaborative Memory Network (ICMN) for ICF, which bases on the architecture of Memory Networks. Besides, a neural attention mechanism is adopted to focus on the most important historically interacted items. The core of our ICMN is the cooperation of external and internal memory and the contribution of the neural attention mechanism. Compare to the state-of-the-art ICF methods, our ICMN possesses the merit of powerful representation capability. Extensive experiments on two datasets demonstrate the effectiveness of ICMN. To the best of our knowledge, this is the first attempt that applies memory networks for ICF. |
| first_indexed | 2024-12-17T05:13:33Z |
| format | Article |
| id | doaj.art-a0ac4161ea5d451588ee6511173ee578 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:13:33Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a0ac4161ea5d451588ee6511173ee5782022-12-21T22:02:11ZengIEEEIEEE Access2169-35362020-01-01821302721303710.1109/ACCESS.2020.30393809264159Item-Based Collaborative Memory Networks for RecommendationDewen Seng0https://orcid.org/0000-0002-3527-4998Guangsen Chen1https://orcid.org/0000-0001-6450-9349Qiyan Zhang2https://orcid.org/0000-0001-6089-1608School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, ChinaSchool of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, ChinaSchool of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, ChinaItem-based Collaborative Filtering (ICF or Item-based CF) has been widely applied for recommender systems in industrial scenarios, owing to its efficiency in user preference modeling and flexibility in online personalization. It captures a user's preference from his or her historically interacted items, recommending new items that are the most similar to the user's preference. Recently, several works propose advanced neural network architectures to learn item similarities from rating data, by modeling items as latent vectors and learning model parameters by optimizing a recommendation-aware objective function. While much literature attempts to use classical neural networks such as multi-layer perception (MLP), to learn item similarities, there has been relatively less work employing memory networks for ICF, which can more accurately record the detailed information about entities than classical neural networks. Therefore, in this paper, we propose a powerful Item-based Collaborative Memory Network (ICMN) for ICF, which bases on the architecture of Memory Networks. Besides, a neural attention mechanism is adopted to focus on the most important historically interacted items. The core of our ICMN is the cooperation of external and internal memory and the contribution of the neural attention mechanism. Compare to the state-of-the-art ICF methods, our ICMN possesses the merit of powerful representation capability. Extensive experiments on two datasets demonstrate the effectiveness of ICMN. To the best of our knowledge, this is the first attempt that applies memory networks for ICF.https://ieeexplore.ieee.org/document/9264159/End-to-end memory networksitem-based collaborative filteringattention mechanism |
| spellingShingle | Dewen Seng Guangsen Chen Qiyan Zhang Item-Based Collaborative Memory Networks for Recommendation IEEE Access End-to-end memory networks item-based collaborative filtering attention mechanism |
| title | Item-Based Collaborative Memory Networks for Recommendation |
| title_full | Item-Based Collaborative Memory Networks for Recommendation |
| title_fullStr | Item-Based Collaborative Memory Networks for Recommendation |
| title_full_unstemmed | Item-Based Collaborative Memory Networks for Recommendation |
| title_short | Item-Based Collaborative Memory Networks for Recommendation |
| title_sort | item based collaborative memory networks for recommendation |
| topic | End-to-end memory networks item-based collaborative filtering attention mechanism |
| url | https://ieeexplore.ieee.org/document/9264159/ |
| work_keys_str_mv | AT dewenseng itembasedcollaborativememorynetworksforrecommendation AT guangsenchen itembasedcollaborativememorynetworksforrecommendation AT qiyanzhang itembasedcollaborativememorynetworksforrecommendation |