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Graph bottlenecked social recommendation
With the emergence of social networks, social recommendation has become an essential
technique for personalized services. Recently, graph-based social recommendations have …
technique for personalized services. Recently, graph-based social recommendations have …
Contextual Distillation Model for Diversified Recommendation
The diversity of recommendation is equally crucial as accuracy in improving user
experience. Existing studies, eg, Determinantal Point Process (DPP) and Maximal Marginal …
experience. Existing studies, eg, Determinantal Point Process (DPP) and Maximal Marginal …
Popularity-aware alignment and contrast for mitigating popularity bias
Collaborative Filtering~(CF) typically suffers from the significant challenge of popularity bias
due to the uneven distribution of items in real-world datasets. This bias leads to a significant …
due to the uneven distribution of items in real-world datasets. This bias leads to a significant …
Llm4dsr: Leveraing large language model for denoising sequential recommendation
B Wang, F Liu, J Chen, Y Wu, X Lou, J Wang… - ar** Matters: An Unsupervised Alignment enhanced Cross-Domain Cold-Start Recommendation
Cold-start recommendation is a long-standing challenge when presenting potential
preferred items to new users. Most empirical studies leverage side information to promote …
preferred items to new users. Most empirical studies leverage side information to promote …
Boosting Multimedia Recommendation via Separate Generic and Unique Awareness
Multimedia recommendation, which incorporates various modalities (eg, images, texts, etc.)
into user or item representation to improve recommendation quality, has received …
into user or item representation to improve recommendation quality, has received …
Personalized Denoising Implicit Feedback for Robust Recommender System
While implicit feedback is foundational to modern recommender systems, factors such as
human error, uncertainty, and ambiguity in user behavior inevitably introduce significant …
human error, uncertainty, and ambiguity in user behavior inevitably introduce significant …
When SparseMoE Meets Noisy Interactions: An Ensemble View on Denoising Recommendation
Learning user preferences from implicit feedback is one of the core challenges in
recommendation. The difficulty lies in the potential noise within implicit feedback. Therefore …
recommendation. The difficulty lies in the potential noise within implicit feedback. Therefore …
Model-Agnostic Social Network Refinement with Diffusion Models for Robust Social Recommendation
Social recommendations (SRs) aim to enhance preference modeling by integrating social
networks. However, their effectiveness is mainly constrained by two factors: the noisy social …
networks. However, their effectiveness is mainly constrained by two factors: the noisy social …