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Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors

Authors:

Christina Göpfert,

Deepak Ramachandran,

Craig BoutilierAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 2411 - 2421

https://doi.org/10.1145/3485447.3512113

Published: 25 April 2022 Publication History

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Abstract

Interactive recommender systems (RSs) allow users to express intent, preferences and contexts in a rich fashion, often using natural language. One challenge in using such feedback is inferring a user’s semantic intent from the open-ended terms used to describe an item, and using it to refine recommendation results. Leveraging concept activation vectors (CAVs) [21], we develop a framework to learn a representation that captures the semantics of such attributes and connects them to user preferences and behaviors in RSs. A novel feature of our approach is its ability to distinguish objective and subjective attributes and associate different senses with different users. Using synthetic and real-world datasets, we show that our CAV representation accurately interprets users’ subjective semantics, and can improve recommendations via interactive critiquing.

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https://doi.org/10.1109/INDIN51400.2023.10218170

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Information & Contributors

Information

Published In

cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

Copyright © 2022 Owner/Author.

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

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Published: 25 April 2022

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

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Cited By

Decker TLebacher MTresp V(2023)Explaining Deep Neural Networks for Bearing Fault Detection with Vibration Concepts2023 IEEE 21st International Conference on Industrial Informatics (INDIN)10.1109/INDIN51400.2023.10218170(1-6)Online publication date: 18-Jul-2023
https://doi.org/10.1109/INDIN51400.2023.10218170

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