Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation?

Autor:	Malitesta, Daniele, Rossi, Emanuele, Pomo, Claudio, Di Noia, Tommaso, Malliaros, Fragkiskos D.
Rok vydání:	2024
Předmět:	Computer Science - Information Retrieval
Druh dokumentu:	Working Paper
Popis:	Generally, items with missing modalities are dropped in multimodal recommendation. However, with this work, we question this procedure, highlighting that it would further damage the pipeline of any multimodal recommender system. First, we show that the lack of (some) modalities is, in fact, a widely-diffused phenomenon in multimodal recommendation. Second, we propose a pipeline that imputes missing multimodal features in recommendation by leveraging traditional imputation strategies in machine learning. Then, given the graph structure of the recommendation data, we also propose three more effective imputation solutions that leverage the item-item co-purchase graph and the multimodal similarities of co-interacted items. Our method can be plugged into any multimodal RSs in the literature working as an untrained pre-processing phase, showing (through extensive experiments) that any data pre-filtering is not only unnecessary but also harmful to the performance. Comment: Accepted at CIKM 2024 in the short paper track
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2408.11767 Zobrazit plný text záznamu View this record from Arxiv