Indexable Representation Learning for Fast Retrieval of Matrix Factorization-Based Top-k Recommendations. (continue)
Speaker: Lê Duy Dũng (College of Engineering and Computer Science, VinU)

Time: 9h30, Thursday, August 26, 2021

Link Online: meet.google.com/qio-vuvf-mro

Tóm tắt: Top-k recommendation seeks to deliver a personalized list of k items to each individual user. An established methodology in the literature based on matrix factorization (MF), which usually represents users and items as vectors in low-dimensional space, is an effective approach to recommender systems. A typical matrix factorization recommender system has two main phases: preference elicitation and recommendation retrieval. The former analyzes user-generated data to learn user preferences and item characteristics in the form of latent feature vectors, whereas the latter ranks the candidate items based on the learnt vectors and returns the top-k items from the ranked list. For preference elicitation, there have been numerous works to build accurate MF-based algorithms that can learn from large datasets. However, for the recommendation retrieval phase, naively scanning a large number of items to identify the few most relevant ones may inhibit truly real-time applications. In this talk, I will present several solutions that attempt to address this issue by indexing approach, which transforms the item vectors and stores them in a data structure that supports efficient candidate filtering upon query, probably in sub-linear time with respect to the number of items.

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