Memory network-based interpreter of user preferences in content-aware recommender systems

• Main Authors: TRAN, Nhu Thuat, LAUW, Hady W.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Content-aware recommender; Memory Network-Based Interpreter; Interpreting user preferences; Databases and Information Systems; Numerical Analysis and Computation; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/8340; https://ink.library.smu.edu.sg/context/sis_research/article/9343/viewcontent/3625239_pvoa_cc_by.pdf
• Institution: Singapore Management University

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.