METRIC: Multiple preferences learning with refined item attributes for multimodal recommendation
In recent years, there has been a burgeoning interest in multimodal recommender systems, which integrate various data types to achieve more personalized recommendations. Despite this, the effective incorporation of user preferences for multimodal data and the exploration of inherent semantic relatio...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-05-01
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| Series: | Journal of Information and Intelligence |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949715925000150 |
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| author | Yunfei Zhao Jie Guo Longyu Wen Letian Wang |
| author_facet | Yunfei Zhao Jie Guo Longyu Wen Letian Wang |
| author_sort | Yunfei Zhao |
| collection | DOAJ |
| description | In recent years, there has been a burgeoning interest in multimodal recommender systems, which integrate various data types to achieve more personalized recommendations. Despite this, the effective incorporation of user preferences for multimodal data and the exploration of inherent semantic relationships between modalities still need to be explored. Prior research typically utilizes multimodal data to construct item graphs, often overlooking the nuanced details within the data. As a result, these studies fail to thoroughly examine the semantic relationships between items and user behavioral patterns. Our proposed approach, METRIC, addresses this gap by delving deeper into multimodal information. METRIC consists of two primary modules: the multiple preference modelling (MPM) module and the item semantic enhancement (ISE) module. The ISE module performs relational mining across multiple attributes, leveraging the semantic structural relationships inherent in items. In contrast, the MPM module enables users to articulate their preferences across different modalities and facilitates adaptive fusion through an attention mechanism. This approach not only improves precision in capturing user preferences and interests but also minimizes interference from varying modalities. Our extensive experiments on three benchmark datasets substantiate METRIC's superiority and the efficacy of its core components. |
| format | Article |
| id | doaj-art-cd4eb0eee3d34689a223eac2e9efdf1e |
| institution | OA Journals |
| issn | 2949-7159 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Information and Intelligence |
| spelling | doaj-art-cd4eb0eee3d34689a223eac2e9efdf1e2025-08-20T02:35:23ZengKeAi Communications Co., Ltd.Journal of Information and Intelligence2949-71592025-05-013324225610.1016/j.jiixd.2025.04.001METRIC: Multiple preferences learning with refined item attributes for multimodal recommendationYunfei Zhao0Jie Guo1Longyu Wen2Letian Wang3State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, ChinaState Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China; Corresponding author.Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, ChinaHangzhou Institute of Technology, Xidian University, Hangzhou 311231, ChinaIn recent years, there has been a burgeoning interest in multimodal recommender systems, which integrate various data types to achieve more personalized recommendations. Despite this, the effective incorporation of user preferences for multimodal data and the exploration of inherent semantic relationships between modalities still need to be explored. Prior research typically utilizes multimodal data to construct item graphs, often overlooking the nuanced details within the data. As a result, these studies fail to thoroughly examine the semantic relationships between items and user behavioral patterns. Our proposed approach, METRIC, addresses this gap by delving deeper into multimodal information. METRIC consists of two primary modules: the multiple preference modelling (MPM) module and the item semantic enhancement (ISE) module. The ISE module performs relational mining across multiple attributes, leveraging the semantic structural relationships inherent in items. In contrast, the MPM module enables users to articulate their preferences across different modalities and facilitates adaptive fusion through an attention mechanism. This approach not only improves precision in capturing user preferences and interests but also minimizes interference from varying modalities. Our extensive experiments on three benchmark datasets substantiate METRIC's superiority and the efficacy of its core components.http://www.sciencedirect.com/science/article/pii/S2949715925000150Multimodal recommendationGraph convolution networkEmbedding enhancementPreference enhancement |
| spellingShingle | Yunfei Zhao Jie Guo Longyu Wen Letian Wang METRIC: Multiple preferences learning with refined item attributes for multimodal recommendation Journal of Information and Intelligence Multimodal recommendation Graph convolution network Embedding enhancement Preference enhancement |
| title | METRIC: Multiple preferences learning with refined item attributes for multimodal recommendation |
| title_full | METRIC: Multiple preferences learning with refined item attributes for multimodal recommendation |
| title_fullStr | METRIC: Multiple preferences learning with refined item attributes for multimodal recommendation |
| title_full_unstemmed | METRIC: Multiple preferences learning with refined item attributes for multimodal recommendation |
| title_short | METRIC: Multiple preferences learning with refined item attributes for multimodal recommendation |
| title_sort | metric multiple preferences learning with refined item attributes for multimodal recommendation |
| topic | Multimodal recommendation Graph convolution network Embedding enhancement Preference enhancement |
| url | http://www.sciencedirect.com/science/article/pii/S2949715925000150 |
| work_keys_str_mv | AT yunfeizhao metricmultiplepreferenceslearningwithrefineditemattributesformultimodalrecommendation AT jieguo metricmultiplepreferenceslearningwithrefineditemattributesformultimodalrecommendation AT longyuwen metricmultiplepreferenceslearningwithrefineditemattributesformultimodalrecommendation AT letianwang metricmultiplepreferenceslearningwithrefineditemattributesformultimodalrecommendation |