Affiliation:
1. School of Computing and Information Systems, Singapore Management University, Singapore
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.
Funder
National Research Foundation, Singapore under its AI Singapore Programme
Publisher
Association for Computing Machinery (ACM)
Subject
Artificial Intelligence,Theoretical Computer Science
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