most citedEncode Me If You Can: Learning Universal User Representations via Event Sequence Autoencoding

1 citations · 1 across the 2 of their papers we have counts for

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2 papers

cs.IR2025★ 1 cited

Encode Me If You Can: Learning Universal User Representations via Event Sequence Autoencoding

Anton Klenitskiy, Artem Fatkulin, Daria Denisova +2

Building universal user representations that capture the essential aspects of user behavior is a crucial task for modern machine learning systems. In real-world applications, a use…

cs.IR2025

Let It Go? Not Quite: Addressing Item Cold Start in Sequential Recommendations with Content-Based Initialization

Anton Pembek, Artem Fatkulin, Anton Klenitskiy +1

Many sequential recommender systems suffer from the cold start problem, where items with few or no interactions cannot be effectively used by the model due to the absence of a trai…