1 citations · 1 across the 3 of their papers we have counts for
4 papers
Benefiting from Negative yet Informative Feedback by Contrasting Opposing Sequential Patterns
Veronika Ivanova, Evgeny Frolov, Alexey Vasilev
We consider the task of learning from both positive and negative feedback in a sequential recommendation scenario, as both types of feedback are often present in user interactions.…
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…
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…
Time to Split: Exploring Data Splitting Strategies for Offline Evaluation of Sequential Recommenders
Danil Gusak, Anna Volodkevich, Anton Klenitskiy +2
Modern sequential recommender systems, ranging from lightweight transformer-based variants to large language models, have become increasingly prominent in academia and industry due…