Multimedia recommender systems: Algorithms and challenges

Yashar Deldjoo, Markus Schedl, Balázs Hidasi, Yinwei Wei, Xiangnan He

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review

13 Citations (Scopus)

Abstract

This chapter studies state-of-the-art research related to multimedia recommender systems (MMRS), focusing on methods that integrate multimedia content as side information to various recommendation models. The multimedia features are then used by an MMRS to recommend either (1) media items from which the features were derived, or (2) non-media items utilizing the features obtained from a proxy multimedia representation of the item (e.g., images of clothes). We first outline the key considerations and challenges that must be taken into account while developing an MMRS. We then discuss the most popular multimedia content processing approaches to produce item representations that may be utilized as side information in an MMRS. Finally, we discuss recent state-of-the-art MMRS algorithms, which we classify and present according to classical hybrid models (e.g., VBPR), neural approaches, and graph-based approaches. Throughout this work, we mentioned several use-cases of MMRSs in the recommender systems research across several domains or products types such as food, fashion, music, videos, and so forth. We hope this chapter provides fresh insights into the nexus of multimedia and recommender systems, which could be exploited to broaden the frontier in the field.

Original languageEnglish
Title of host publicationRecommender Systems Handbook
EditorsFrancesco Ricci, Lior Rokach, Bracha Shapira
Place of PublicationNew York NY USA
PublisherSpringer
Chapter25
Pages973-1014
Number of pages42
Edition3rd
ISBN (Electronic)9781071621974
ISBN (Print)9781071621967
DOIs
Publication statusPublished - 2022
Externally publishedYes

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