Latent User Intent Modeling in Recommender Systems
Offered By: Toronto Machine Learning Series (TMLS) via YouTube
Course Description
Overview
Explore the concept of latent user intent modeling in recommender systems through this 25-minute conference talk from the Toronto Machine Learning Series (TMLS). Presented by Bo Chang, a Software Engineer at Google Brain, the talk delves into the challenges faced by current sequential recommender systems, which primarily rely on users' item-level interaction history to capture topical interests. Learn how these systems often lack a high-level understanding of user intent and why explicitly defining and enumerating all possible user intents is a complex task. Discover a proposed solution using latent variable models to capture user intents as latent variables through encoding and decoding user behavior signals. Gain insights into the practical application of this approach in a large industrial recommender system, and understand how it can potentially improve the accuracy and effectiveness of recommendation algorithms.
Syllabus
Latent User Intent Modeling in Recommender Systems
Taught by
Toronto Machine Learning Series (TMLS)
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