arXiv Machine Learning By Fiza Husain, Anson Bastos, Anjaly Parayil, Ayush Choure, Chetan Bansal, Rujia Wang, Saravan Rajmohan

Attention Enhanced Entity Recommendation for Intelligent Monitoring in Cloud Systems

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arXiv:2510. 20640v2 Announce Type: replace Abstract: In this paper, we present DiRecGNN, an attention-enhanced entity recommendation framework for monitoring cloud services at Microsoft.

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arXiv AI
Jun 26

The Best of the Two Worlds: Harmonizing Semantic and Hash IDs for Sequential Recommendation

arXiv:2512. 10388v3 Announce Type: replace-cross Abstract: Conventional Sequential Recommender Systems (SRS) typically assign unique hash IDs (HID) to construct item embeddings, which mainly capture collaborative signals from historical user-item interactions.

By Ziwei Liu, Yejing Wang, Wanyu Wang, Wang Zejian, Qidong Liu, Zijian Zhang, Chong Chen, Wei Huang, Xiangyu Zhao
arXiv AI
Jul 21

WHALE: A Scalable Unified Model for Recommendation with Wukong-HSTU Architecture

arXiv:2607. 17017v1 Announce Type: cross Abstract: As scalability becomes increasingly important in recommendation modeling, recent architectures have advanced the modeling of two broad sources of ranking signals along separate paths: non-sequence features, including user, item, context, and cross features; and sequence features from user behavior histories.

By Renqin Cai, Dawei Sun, Yuanjun Yao, Zhiyong Wang, Velvin Fu, Maggie Zhuang, Yu Shi, Zhongnan Fang, Xuan Cao, Jing Qian, Rui Li
arXiv Computer Vision
6d ago

SOLAR: SVD-Optimized Lifelong Attention for Recommendation

arXiv:2603.02561v2 Announce Type: replace-cross Abstract: Attention mechanism remains the defining operator in Transformers since it provides expressive global credit assignment, yet its quadratic co...

By Chenghao Zhang, Chao Feng, Yuanhao Pu, Xunyong Yang, Wenhui Yu, Xiang Li, Chunjie Chen, Kaiqiao Zhan
arXiv Machine Learning
Aug 11

PreGress: Ranking-Native Pre-training and Prompting for Graph Node Ranking

arXiv:2608. 09016v1 Announce Type: cross Abstract: Node ranking is a fundamental problem in graph information retrieval, measuring the relative importance of nodes and supporting a wide range of applications such as influence analysis, recommendation, and graph-based retrieval augmented generation.

By Lujie Ban, Jiasheng shi, Yingli Zhou, Kaiwen Xue, Daiyin Wang, Xubin Li, Shuanghua Li, Chenhao Ma