4 accepted papers
Federated recommendation (FR) facilitates collaborative training by aggregating local models from massive devices, enabling client-specific personalization while ensuring privacy. However, we empirically and theoretically demonstrate that server-side aggregation can undermine client-side personaliza
Causal Inference (CI) plays a crucial role in building unbiased recommender systems. However, most current CI-based debiasing methods only pay attention on either popularity bias or conformity bias. This paper presents a Disentangled Counterfactual Reasoning framework to alleviate dual biases in rec…
Hashing-based recommendation (HR) methods, whose core idea is mapping users and items into hamming space, are common practice to improve item retrieval efficiency. However, existing HR fails to align optimization objective (i.e., Bayesian Personalized Ranking) and evaluation metric (i.e., Recall), l…
We address the efficiency problem of personalized ranking from implicit feedback by hashing users and items with binary codes, so that top-N recommendation can be fast executed in a Hamming space by bit operations. However, current hashing methods for top-N recommendation fail to align their learnin…