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Found 5 papers, 2 papers with code
Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering
While conventional CF models are known for facing the challenges of the popularity bias that favors popular items, one may wonder "Whether the existing graph-based CF models alleviate or exacerbate popularity bias of recommender systems?"
RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender System
In summary, the RL4RS (Reinforcement Learning for Recommender Systems), a new resource with special concerns on the reality gaps, contains two real-world datasets, data understanding tools, tuned simulation environments, related advanced RL baselines, batch RL baselines, and counterfactual policy evaluation algorithms.
Intent Disentanglement and Feature Self-supervision for Novel Recommendation
Improving the recommendation of tail items can promote novelty and bring positive effects to both users and providers, and thus is a desirable property of recommender systems.
Recommending Accurate and Diverse Items Using Bilateral Branch Network
Specifically, we encode domain level diversity by adaptively balancing accurate recommendation in the conventional branch and diversified recommendation in the adaptive branch of a bilateral branch network.
Solving Cold Start Problem in Recommendation with Attribute Graph Neural Networks
More importantly, for a cold start user/item that does not have any interactions, such methods are unable to learn the preference embedding of the user/item since there is no link to this user/item in the graph.
