Zobrazeno 1 - 10
of 274
pro vyhledávání: '"Sang, Lei"'
Click-Through Rate (CTR) prediction plays a vital role in recommender systems, online advertising, and search engines. Most of the current approaches model feature interactions through stacked or parallel structures, with some employing knowledge dis
Externí odkaz:
http://arxiv.org/abs/2411.07508
Cross-domain recommendation (CDR) aims to improve recommendation accuracy in sparse domains by transferring knowledge from data-rich domains. However, existing CDR methods often assume the availability of user-item interaction data across domains, ov
Externí odkaz:
http://arxiv.org/abs/2409.03294
Self-supervised learning (SSL) has recently attracted significant attention in the field of recommender systems. Contrastive learning (CL) stands out as a major SSL paradigm due to its robust ability to generate self-supervised signals. Mainstream gr
Externí odkaz:
http://arxiv.org/abs/2407.19692
Contrastive Learning (CL)-based recommender systems have gained prominence in the context of Heterogeneous Graph (HG) due to their capacity to enhance the consistency of representations across different views. However, existing frameworks often negle
Externí odkaz:
http://arxiv.org/abs/2407.17234
Deep & Cross Network and its derivative models have become an important paradigm for click-through rate (CTR) prediction due to their effective balance between computational cost and performance. However, these models face four major limitations: (1)
Externí odkaz:
http://arxiv.org/abs/2407.13349
Intent modeling has attracted widespread attention in recommender systems. As the core motivation behind user selection of items, intent is crucial for elucidating recommendation results. The current mainstream modeling method is to abstract the inte
Externí odkaz:
http://arxiv.org/abs/2405.09042
Social recommendation leverages social network to complement user-item interaction data for recommendation task, aiming to mitigate the data sparsity issue in recommender systems. However, existing social recommendation methods encounter the followin
Externí odkaz:
http://arxiv.org/abs/2405.04942
Effective feature interaction modeling is critical for enhancing the accuracy of click-through rate (CTR) prediction in industrial recommender systems. Most of the current deep CTR models resort to building complex network architectures to better cap
Externí odkaz:
http://arxiv.org/abs/2405.03167
Heterogeneous Graphs (HGs) can effectively model complex relationships in the real world by multi-type nodes and edges. In recent years, inspired by self-supervised learning, contrastive Heterogeneous Graphs Neural Networks (HGNNs) have shown great p
Externí odkaz:
http://arxiv.org/abs/2404.02810
Cross-domain recommendation (CDR) aims to enhance recommendation accuracy in a target domain with sparse data by leveraging rich information in a source domain, thereby addressing the data-sparsity problem. Some existing CDR methods highlight the adv
Externí odkaz:
http://arxiv.org/abs/2403.03600