13 citations · 22 across the 9 of their papers we have counts for
9 papers
Bradley-Terry Rankings for Recommender Systems Across Dataset Taxonomies
Ekaterina Grishina, Stepan Kuznetsov, Askar Tsyganov +8
The ranking of recommendation algorithms is a challenging problem since model performance is sensitive to dataset characteristics such as sparsity, sequential structure, and scale.…
Cross-Representation Knowledge Transfer for Improved Sequential Recommendations
Artur Gimranov, Viacheslav Yusupov, Elfat Sabitov +4
Transformer architectures, capable of capturing sequential dependencies in the history of user interactions, have become the dominant approach in sequential recommender systems. De…
Position-Aware Sequential Attention for Accurate Next Item Recommendations
Timur Nabiev, Evgeny Frolov
Sequential self-attention models usually rely on additive positional embeddings, which inject positional information into item representations at the input. In the absence of posit…
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.…
Recommendation Is a Dish Better Served Warm
Danil Gusak, Nikita Sukhorukov, Evgeny Frolov
In modern recommender systems, experimental settings typically include filtering out cold users and items based on a minimum interaction threshold. However, these thresholds are of…
Maximum Impact with Fewer Features: Efficient Feature Selection for Cold-Start Recommenders through Collaborative Importance Weighting
Nikita Sukhorukov, Danil Gusak, Evgeny Frolov
Cold-start challenges in recommender systems necessitate leveraging auxiliary features beyond user-item interactions. However, the presence of irrelevant or noisy features can degr…
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…
Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization
Abdulaziz Samra, Evgeney Frolov, Alexey Vasilev +2
Data sparsity has been one of the long-standing problems for recommender systems. One of the solutions to mitigate this issue is to exploit knowledge available in other source doma…
End-to-End Graph-Sequential Representation Learning for Accurate Recommendations
Vladimir Baikalov, Evgeny Frolov
Recent recommender system advancements have focused on developing sequence-based and graph-based approaches. Both approaches proved useful in modeling intricate relationships withi…