Formalizing Multimedia Recommendation through Multimodal Deep Learning
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909183358533632 |
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| author | Malitesta, Daniele Cornacchia, Giandomenico Pomo, Claudio Merra, Felice Antonio Di Noia, Tommaso Di Sciascio, Eugenio |
| author_facet | Malitesta, Daniele Cornacchia, Giandomenico Pomo, Claudio Merra, Felice Antonio Di Noia, Tommaso Di Sciascio, Eugenio |
| contents | Recommender systems (RSs) offer personalized navigation experiences on online platforms, but recommendation remains a challenging task, particularly in specific scenarios and domains. Multimodality can help tap into richer information sources and construct more refined user/item profiles for recommendations. However, existing literature lacks a shared and universal schema for modeling and solving the recommendation problem through the lens of multimodality. This work aims to formalize a general multimodal schema for multimedia recommendation. It provides a comprehensive literature review of multimodal approaches for multimedia recommendation from the last eight years, outlines the theoretical foundations of a multimodal pipeline, and demonstrates its rationale by applying it to selected state-of-the-art approaches. The work also conducts a benchmarking analysis of recent algorithms for multimedia recommendation within Elliot, a rigorous framework for evaluating recommender systems. The main aim is to provide guidelines for designing and implementing the next generation of multimodal approaches in multimedia recommendation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_05273 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Formalizing Multimedia Recommendation through Multimodal Deep Learning Malitesta, Daniele Cornacchia, Giandomenico Pomo, Claudio Merra, Felice Antonio Di Noia, Tommaso Di Sciascio, Eugenio Information Retrieval Recommender systems (RSs) offer personalized navigation experiences on online platforms, but recommendation remains a challenging task, particularly in specific scenarios and domains. Multimodality can help tap into richer information sources and construct more refined user/item profiles for recommendations. However, existing literature lacks a shared and universal schema for modeling and solving the recommendation problem through the lens of multimodality. This work aims to formalize a general multimodal schema for multimedia recommendation. It provides a comprehensive literature review of multimodal approaches for multimedia recommendation from the last eight years, outlines the theoretical foundations of a multimodal pipeline, and demonstrates its rationale by applying it to selected state-of-the-art approaches. The work also conducts a benchmarking analysis of recent algorithms for multimedia recommendation within Elliot, a rigorous framework for evaluating recommender systems. The main aim is to provide guidelines for designing and implementing the next generation of multimodal approaches in multimedia recommendation. |
| title | Formalizing Multimedia Recommendation through Multimodal Deep Learning |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2309.05273 |