arXiv:2607. 17461v1 Announce Type: cross Abstract: The Matthew effect is a notorious issue in Recommender Systems (RSs), \emph{i.
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arXiv:2607. 18609v1 Announce Type: cross Abstract: The Matthew effect is a big challenge in Recommender Systems (RSs), where popular items tend to receive increasing attention, while less popular ones are often overlooked, perpetuating existing disparities.
FacetCRS is a conversational recommender system that tackles the filter‑bubble problem by learning multi‑faceted user preferences—entity, word, context, and review facets—through natural language interactions. The framework adaptively models these preference facets and incorporates external knowledge to provide diverse recommendations. Experiments on two benchmark datasets show that FacetCRS outperforms existing methods in reducing filter bubbles and improving recommendation quality.
arXiv:2506. 07449v2 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLMs) have driven their adoption in recommender systems through Retrieval-Augmented Generation (RAG) frameworks.
arXiv:2608. 15055v1 Announce Type: new Abstract: Hypergraphs effectively model higher-order groupwise relationships beyond pairwise interactions, while pretrained language models (PLMs) and large language models (LLMs) provide rich semantic understanding from textual attributes.
arXiv:2608. 09605v1 Announce Type: cross Abstract: Large Language Models (LLMs) have emerged as powerful tools for improving recommendation systems.
arXiv:2506. 10520v5 Announce Type: replace-cross Abstract: Graph-based multi-task learning at billion-scale presents a significant challenge, as different tasks correspond to distinct billion-scale graphs.