.png){kind=icon}
.png){kind=icon}
Return
Exploring Cross-Modality Utilization in Recommender Systems
.png){kind=icon}
OA
AI
DOI：10.1109/MIC.2021.3059027.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Data models
Visualization
Recommender systems
Analytical models
Predictive models
Computational modeling
Matrices
Multimodal Recommender Systems
Multimodality
Cross Modality 1 MULTIMODAL RECOMMENDER SYSTEMS Modern online applications
such as e-commerce websites and online sharing platforms
rely heavily on recommender systems to guide their users in browsing the myriad of options offered to them
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.4
Papers:
2.0K
Citations:
2.0K