Publication Type

Journal Article

Version

publishedVersion

Publication Date

12-2023

Abstract

This article introduces a novel architecture for two objectives recommendation and interpretability in a unified model. We leverage textual content as a source of interpretability in content-aware recommender systems. The goal is to characterize user preferences with a set of human-understandable attributes, each is described by a single word, enabling comprehension of user interests behind item adoptions. This is achieved via a dedicated architecture, which is interpretable by design, involving two components for recommendation and interpretation. In particular, we seek an interpreter, which accepts holistic user’s representation from a recommender to output a set of activated attributes describing user preferences. Besides encoding interpretability properties such as fidelity, conciseness and diversity, the proposed memory network-based interpreter enables the generalization of user representation by discovering relevant attributes that go beyond her adopted items’ textual content. We design experiments involving both human- and functionally-grounded evaluations of interpretability. Results on four real-world datasets show that our proposed model not only discovers highly relevant attributes for interpreting user preferences, but also enjoys comparable or better recommendation accuracy than a series of baselines.

Keywords

Content-aware recommender, Memory Network-Based Interpreter, Interpreting user preferences

Discipline

Databases and Information Systems | Numerical Analysis and Computation | Software Engineering

Research Areas

Data Science and Engineering

Publication

ACM Transactions on Intelligent Systems and Technology

Volume

14

Issue

6

First Page

1

Last Page

28

ISSN

2157-6904

Identifier

10.1145/3625239

Publisher

ACM

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Additional URL

https://doi.org/10.1145/3625239

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