Exploring cross-modality utilization in recommender systems
Multimodal recommender systems alleviate the sparsity of historical user-item interactions. They are commonly catalogued based on the type of auxiliary data (modality) they leverage, such as preference data plus user-network (social), user/item texts (textual), or item images (visual) respectively....
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| Main Authors: | , , , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2021
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/5950 https://ink.library.smu.edu.sg/context/sis_research/article/6953/viewcontent/ic21.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | Multimodal recommender systems alleviate the sparsity of historical user-item interactions. They are commonly catalogued based on the type of auxiliary data (modality) they leverage, such as preference data plus user-network (social), user/item texts (textual), or item images (visual) respectively. One consequence of this categorization is the tendency for virtual walls to arise between modalities. For instance, a study involving images would compare to only baselines ostensibly designed for images. However, a closer look at existing models' statistical assumptions about any one modality would reveal that many could work just as well with other modalities. Therefore, we pursue a systematic investigation into several research questions: which modality one should rely on, whether a model designed for one modality may work with another, which model to use for a given modality. We conduct cross-modality and cross-model comparisons and analyses, yielding insightful results pointing to interesting future research directions for multimodal recommender systems. |
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