Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

10-2021

Abstract

Recommender systems typically learn from user-item preference data such as ratings and clicks. This information is sparse in nature, i.e., observed user-item preferences often represent less than 5% of possible interactions. One promising direction to alleviate data sparsity is to leverage auxiliary information that may encode additional clues on how users consume items. Examples of such data (referred to as modalities) are social networks, item’s descriptive text, product images. The objective of this tutorial is to offer a comprehensive review of recent advances to represent, transform and incorporate the different modalities into recommendation models. Moreover, through practical hands-on sessions, we consider cross model/modality comparisons to investigate the importance of different methods and modalities. The hands-on exercises are conducted with Cornac (https://cornac.preferred.ai ), a comparative framework for multimodal recommender systems. The materials are made available on https://preferred.ai/recsys21-tutorial/.

Keywords

Recommender systems, Auxiliary information, Cross model, Data sparsity, Hands-on exercise, Learn+, Multi-modal, Preference data, Product images

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

RecSys'21: Proceedings of the 15th ACM Conference on Recommender Systems, September 27 - October 1, Virtual

First Page

834

Last Page

837

ISBN

9781450384582

Identifier

10.1145/3460231.3473324

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3460231.3473324

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