Sparsity-aware personalized recommender system via meta-learning

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Wang, Junting

Contributors dc:contributor

• Sundaram, Hari

• Recommendation System
• Collaborative Filtering
• Meta Learning
• Few-shot learning

Statement dc:rights

• Copyright 2022 Junting Wang

Language dc:language

en, eng

Handle dc:identifier

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

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F115612