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Designing a Sequential Recommendation System for Heterogeneous Interactions Using Transformers
In this work, by converting each user's interactions with items into a series of events and basing our architecture on Transformers, we try to enable the use of such a model that takes different types of events into account.
An Improved Hybrid Recommender System: Integrating Document Context-Based and Behavior-Based Methods
One of the main challenges in recommender systems is data sparsity which leads to high variance.
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