Zobrazeno 1 - 10
of 301
pro vyhledávání: '"Yang Chaoqun"'
Autor:
Lin, Xinyu, Yang, Chaoqun, Wang, Wenjie, Li, Yongqi, Du, Cunxiao, Feng, Fuli, Ng, See-Kiong, Chua, Tat-Seng
Large Language Model (LLM)-based generative recommendation has achieved notable success, yet its practical deployment is costly particularly due to excessive inference latency caused by autoregressive decoding. For lossless LLM decoding acceleration,
Externí odkaz:
http://arxiv.org/abs/2410.05165
Federated sequential recommendation (FedSeqRec) has gained growing attention due to its ability to protect user privacy. Unfortunately, the performance of FedSeqRec is still unsatisfactory because the models used in FedSeqRec have to be lightweight t
Externí odkaz:
http://arxiv.org/abs/2410.04927
Federated recommender systems (FedRecs) have emerged as a popular research direction for protecting users' privacy in on-device recommendations. In FedRecs, users keep their data locally and only contribute their local collaborative information by up
Externí odkaz:
http://arxiv.org/abs/2409.07773
Sequential recommender systems have made significant progress. Recently, due to increasing concerns about user data privacy, some researchers have implemented federated learning for sequential recommendation, a.k.a., Federated Sequential Recommender
Externí odkaz:
http://arxiv.org/abs/2406.05387
Federated Recommender Systems (FedRecs) have garnered increasing attention recently, thanks to their privacy-preserving benefits. However, the decentralized and open characteristics of current FedRecs present two dilemmas. First, the performance of F
Externí odkaz:
http://arxiv.org/abs/2403.20107
This paper addresses the problem of group target tracking (GTT), wherein multiple closely spaced targets within a group pose a coordinated motion. To improve the tracking performance, the labeled random finite sets (LRFSs) theory is adopted, and this
Externí odkaz:
http://arxiv.org/abs/2403.13562
With the growing concerns regarding user data privacy, Federated Recommender System (FedRec) has garnered significant attention recently due to its privacy-preserving capabilities. Existing FedRecs generally adhere to a learning protocol in which a c
Externí odkaz:
http://arxiv.org/abs/2311.14968
Cross-Domain Recommendation (CDR) stands as a pivotal technology addressing issues of data sparsity and cold start by transferring general knowledge from the source to the target domain. However, existing CDR models suffer limitations in adaptability
Externí odkaz:
http://arxiv.org/abs/2310.13303
Federated recommender systems (FedRecs) have been widely explored recently due to their ability to protect user data privacy. In FedRecs, a central server collaboratively learns recommendation models by sharing model public parameters with clients, t
Externí odkaz:
http://arxiv.org/abs/2305.08183
Knowledge graphs (KGs) have become important auxiliary information for helping recommender systems obtain a good understanding of user preferences. Despite recent advances in KG-based recommender systems, existing methods are prone to suboptimal perf
Externí odkaz:
http://arxiv.org/abs/2304.12083