Zobrazeno 1 - 10
of 157
pro vyhledávání: '"Pan, Weike"'
Sequential recommendation leverages interaction sequences to predict forthcoming user behaviors, crucial for crafting personalized recommendations. However, the true preferences of a user are inherently complex and high-dimensional, while the observe
Externí odkaz:
http://arxiv.org/abs/2407.17802
Deep learning with differential privacy (DP) has garnered significant attention over the past years, leading to the development of numerous methods aimed at enhancing model accuracy and training efficiency. This paper delves into the problem of train
Externí odkaz:
http://arxiv.org/abs/2405.18194
In real recommendation scenarios, users often have different types of behaviors, such as clicking and buying. Existing research methods show that it is possible to capture the heterogeneous interests of users through different types of behaviors. How
Externí odkaz:
http://arxiv.org/abs/2402.12733
Cross-domain sequential recommendation is an important development direction of recommender systems. It combines the characteristics of sequential recommender systems and cross-domain recommender systems, which can capture the dynamic preferences of
Externí odkaz:
http://arxiv.org/abs/2401.15369
Publikováno v:
The Thirty-Third International Joint Conference on Artificial Intelligence Survey Track. 2024. Pages 7989-7998
Cross-domain sequential recommendation (CDSR) shifts the modeling of user preferences from flat to stereoscopic by integrating and learning interaction information from multiple domains at different granularities (ranging from inter-sequence to intra
Externí odkaz:
http://arxiv.org/abs/2401.04971
Recommender systems is set up to address the issue of information overload in traditional information retrieval systems, which is focused on recommending information that is of most interest to users from massive information. Generally, there is a se
Externí odkaz:
http://arxiv.org/abs/2308.15701
Graph neural networks (GNNs) have gained wide popularity in recommender systems due to their capability to capture higher-order structure information among the nodes of users and items. However, these methods need to collect personal interaction data
Externí odkaz:
http://arxiv.org/abs/2308.01197
The Transformer has emerged as a versatile and effective architecture with broad applications. However, it still remains an open problem how to efficiently train a Transformer model of high utility with differential privacy guarantees. In this paper,
Externí odkaz:
http://arxiv.org/abs/2305.17633
Autor:
Liu, Dugang, Cheng, Pengxiang, Lin, Zinan, Zhang, Xiaolian, Dong, Zhenhua, Zhang, Rui, He, Xiuqiang, Pan, Weike, Ming, Zhong
Debiased recommendation with a randomized dataset has shown very promising results in mitigating the system-induced biases. However, it still lacks more theoretical insights or an ideal optimization objective function compared with the other more wel
Externí odkaz:
http://arxiv.org/abs/2303.11574
Research on debiased recommendation has shown promising results. However, some issues still need to be handled for its application in industrial recommendation. For example, most of the existing methods require some specific data, architectures and t
Externí odkaz:
http://arxiv.org/abs/2302.03419