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Building Visually-aware, Dynamic, and Efficient Recommender Systems

  • Author(s): Kang, Wangcheng
  • Advisor(s): McAuley, Julian
  • et al.
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Abstract

Conventional recommendation models often use the user-item interaction matrix (e.g. ratings) to predict user preferences and generate recommendations. However, it ignores abundant signals and context (e.g. visual signals or temporal context) existing in real-world applications. Moreover, efficiency becomes an essential factor when building large-scale recommendation engines. In this thesis, we seek to extend the conventional recommendation frameworks to adapt new and large-scale application scenarios. Specifically, this thesis includes three directions: (i) Visually-aware Recommendation: we extend recommendation models to visual domains. We develop CNN-based end-to-end learning approaches to make personalized image recommendations and complementary product recommendations. Moreover, beyond recommending existing products, we develop GAN-based preference models to generate new products that are preferred by users; (ii) Dynamic Recommendation: we adapt recommendation models to dynamic environments where user preferences constantly shift. We develop Markov-chain-based and self-attention-based sequential models to respond to the change of users' interests quickly and make more accurate recommendations. (iii) Efficient Recommendation: we consider both time efficiency and space efficiency, where the former seeks to optimize the serving latency, and the latter seeks to reduce the memory consumption of recommendation models.

Main Content

This item is under embargo until July 10, 2022.