University of Illinois Urbana-Champaign

Sparsity-aware personalized recommender system via meta-learning

Wang, Junting

Content Files
WANG-THESIS-2022.pdf

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https://hdl.handle.net/2142/115612

Description

Title
Sparsity-aware personalized recommender system via meta-learning
Author(s)
Wang, Junting
Issue Date
2022-04-27
Director of Research (if dissertation) or Advisor (if thesis)
Sundaram, Hari
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
  • Recommendation System
  • Collaborative Filtering
  • Meta Learning
  • Few-shot learning
Abstract
With the advancement of neural collaborative filtering methods, deep learning methods have become the backbone of modern recommender systems. Neural recommenders provide significant performance gains over conventional methods. However, the recommendations made by them are not truly personalized. Instead, they tend to suggest popular items due to the popularity bias among items. Popularity bias is a fundamental challenge in recommender systems, and it originates from the heavy-tailed distribution of users’ activity data. Modern neural recommenders lack the resolution to rank long-tail items accurately since the interaction data used in the model training is heavily skewed. The biased training results in a biased recommendation model that only recommends the popular subset of the item inventory. In this thesis, we propose a meta-learning framework ProtoCF that effectively eliminates the distribution mismatch between items and learns robust prototype representations for long-tail items. Our experimental results demonstrate that ProtoCF consistently outperforms state-of-the-art approaches on overall recommendation (by 5% Recall@50) while achieving significant gains (of 60-80% Recall@50) for tail items with less than 20 interactions.
Graduation Semester
2022-05
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/115612
Copyright and License Information
Copyright 2022 Junting Wang

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