Exploring User and Item Representation, Justification Generation, and Data Augmentation for Conversational Recommender Systems
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Abstract
Conversational Recommender Systems (CRS) aim to provide personalized and contextualized recommendations through natural language conversations with users. The objective of my proposed dissertation is to capitalize on the recent developments in conversational interfaces to advance the field of Recommender Systems in several directions. I aim to address several problems in recommender systems: user and item representation, justification generation, and data sparsity.
A critical challenge in CRS is learning effective representations of users and items that capture their preferences and characteristics. First, we focus on user representation, where we use a separate corpus of reviews to learn user representation. We attempt to map conversational users into the space of reviewers using semantic similarity between the conversation and the texts of reviews. Second, we improve item representation by incorporating textual features such as item descriptions into the user-item interaction graph, which captures a great deal of semantic and behavioral information unavailable from the purely topological structure of the interaction graph.
Justifications for recommendations enhance the explainability and transparency of CRS; however, existing approaches, such as rule-based and template-based methods, have limitations. In this work, we propose an extractive method using a corpus of reviews to identify relevant information for generating concise and coherent justifications.
We address the challenge of data scarcity for CRS by generating synthetic conversations using SOTA generative pre trained transformers (GPT). These synthetic conversations are used to augment the data used for training the CRS.
In addition, we also evaluate if the GPTs exhibit emerging abilities of CRS (or a non-conversational RecSys) due to the large amount of data they are trained on, which potentially includes the reviews and opinions of users.
Index Terms
- Exploring User and Item Representation, Justification Generation, and Data Augmentation for Conversational Recommender Systems
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Information & Contributors
Information
Published In
July 2023
3567 pages
ISBN:9781450394086
DOI:10.1145/3539618
- General Chairs:
- Hsin-Hsi Chen,
- Wei-Jou (Edward) Duh,
- Hen-Hsen Huang,
- Program Chairs:
- Makoto P. Kato,
- Josiane Mothe,
- Barbara Poblete
Copyright © 2023 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 18 July 2023
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- Amazon
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SIGIR '23
Sponsor:
SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 23 - 27, 2023
Taipei, Taiwan
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Overall Acceptance Rate 792 of 3,983 submissions, 20%
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