Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

3-2021

Abstract

To aid users in choice-making, explainable recommendation models seek to provide not only accurate recommendations but also accompanying explanations that help to make sense of those recommendations. Most of the previous approaches rely on evaluative explanations, assessing the quality of an individual item along some aspects of interest to the user. In this work, we are interested in comparative explanations, the less studied problem of assessing a recommended item in comparison to another reference item.

In particular, we propose to anchor reference items on the previously adopted items in a user's history. Not only do we aim at providing comparative explanations involving such items, but we also formulate comparative constraints involving aspect-level comparisons between the target item and the reference items. The framework allows us to incorporate these constraints and integrate them with recommendation objectives involving both types of subjective and objective aspect-level quality assumptions. Experiments on public datasets of several product categories showcase the efficacies of our methodology as compared to baselines at attaining better recommendation accuracies and intuitive explanations.

Keywords

comparative constraints, explainable recommendation

Discipline

Databases and Information Systems | Data Science | E-Commerce

Research Areas

Data Science and Engineering

Publication

WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, Virtual, March 8-12

First Page

967

Last Page

975

ISBN

9781450382977

Identifier

10.1145/3437963.3441754

Publisher

ACM

City or Country

New York

Embargo Period

5-20-2021

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3437963.3441754

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