Fetching the paper…
Reading the bibliography…
Personalized search systems in e-commerce platforms increasingly involve user interactions with AI assistants, where users consult about products, usage scenarios, and more.
Scarcity and consumer choice behavior
Theo MM Verhallen. 1982 · 1982
Earlier work this paper cites.
One hundred years of forgetting: A quantitative description of retention
David C Rubin and Amy E Wenzel. 1996 · 1996
Earlier work this paper cites.
The probabilistic relevance framework: Bm25 and beyond
Stephen Robertson, Hugo Zaragoza, et al. 2009 · 2009
Earlier work this paper cites.
Combining predictions for accurate recommender systems
Michael Jahrer, Andreas Töscher, and Robert Legenstein. 2010 · 2010
Earlier work this paper cites.
Sabre: a sensitive attribute bucketization and redistribution framework for t-closeness
Jianneng Cao, Panagiotis Karras, Panos Kalnis, and Kian-Lee Tan. 2011 · 2011
Earlier work this paper cites.
Online learning under delayed feedback
Pooria Joulani, Andras Gyorgy, and Csaba Szepesvári. 2013 · 2013
Earlier work this paper cites.
On inverted index compression for search engine efficiency
Matteo Catena, Craig Macdonald, and Iadh Ounis. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Distributed representations of sentences and documents
Quoc Le and Tomas Mikolov. 2014 · 2014
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton. 2015 · 2015
Earlier work this paper cites.
Learning a hierarchical embedding model for personalized product search
Qingyao Ai, Yongfeng Zhang, Keping Bi, Xu Chen, and W Bruce Croft. 2017 · 2017
Earlier work this paper cites.
Deepfm: a factorization-machine based neural network for ctr prediction
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017 · 2017
Earlier work this paper cites.
Consumer journeys: Developing consumer-based strategy
Rebecca Hamilton and Linda L Price. 2019 · 2019
Cited alongside, same era.
Justifying recommendations using distantly-labeled reviews and fine-grained aspects
Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019 · 2019
Cited alongside, same era.
A transformer-based embedding model for personalized product search
Keping Bi, Qingyao Ai, and W Bruce Croft. 2020 · 2020
Cited alongside, same era.
Time interval aware self-attention for sequential recommendation
Jiacheng Li, Yujie Wang, and Julian McAuley. 2020 · 2020
Cited alongside, same era.
Only one room left! how scarcity cues affect booking intentions on hospitality platforms
Timm Teubner and Antje Graul. 2020 · 2020
Cited alongside, same era.
Comparison of min-max normalization and z-score normalization in the k-nearest neighbor (knn) algorithm to test the accuracy of types of breast cancer
When search meets recommendation: Learning disentangled search representation for recommendation
Zihua Si, Zhongxiang Sun, Xiao Zhang, Jun Xu, Xiaoxue Zang, Yang Song, Kun Gai, and Ji-Rong Wen. 2023 · 2023
Later among the works it cites.
Unifiedssr: A unified framework of sequential search and recommendation
Jiayi Xie, Shang Liu, Gao Cong, and Zhenzhong Chen. 2023 · 2023
Later among the works it cites.
Large language models empowered personalized web agents
Hongru Cai, Yongqi Li, Wenjie Wang, Fengbin Zhu, Xiaoyu Shen, Wenjie Li, and Tat-Seng Chua. 2024 · 2024
Later among the works it cites.
Jianlv Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu. 2024 · 2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Henderi Henderi, Tri Wahyuningsih, and Efana Rahwanto. 2021 · 2021
Cited alongside, same era.
Cat: Cross attention in vision transformer
Hezheng Lin, Xing Cheng, Xiangyu Wu, and Dong Shen. 2022 · 2022
Cited alongside, same era.
Long-tail session-based recommendation from calibration
Jiayi Chen, Wen Wu, Liye Shi, Wei Zheng, and Liang He. 2023 · 2023
Cited alongside, same era.
Contrastive learning for user sequence representation in personalized product search
Shitong Dai, Jiongnan Liu, Zhicheng Dou, Haonan Wang, Lin Liu, Bo Long, and Ji-Rong Wen. 2023 · 2023
Cited alongside, same era.
Rating prediction in conversational task assistants with behavioral and conversational-flow features
Rafael Ferreira, David Semedo, and João Magalhães. 2023 · 2023
Cited alongside, same era.
To brush or not to brush: Product rankings, consumer search, and fake orders
Chen Jin, Luyi Yang, and Kartik Hosanagar. 2023 · 2023
Cited alongside, same era.
A zero attention model for personalized product search
Qingyao Ai, Daniel N Hill, SVN Vishwanathan, and W Bruce Croft. 2019a
Cited in the paper.
Fengran Mo, Abbas Ghaddar, Kelong Mao, Mehdi Rezagholizadeh, Boxing Chen, Qun Liu, and Jian-Yun Nie. 2024 · 2024
Later among the works it cites.
Unisar: Modeling user transition behaviors between search and recommendation
Teng Shi, Zihua Si, Jun Xu, Xiao Zhang, Xiaoxue Zang, Kai Zheng, Dewei Leng, Yanan Niu, and Yang Song. 2024 · 2024
Later among the works it cites.
τ \tau -bench: A benchmark for tool-agent-user interaction in real-world domains
Shunyu Yao, Noah Shinn, Pedram Razavi, and Karthik Narasimhan. 2024 · 2024
Later among the works it cites.
Maps: Motivation-aware personalized search via llm-driven consultation alignment
Weicong Qin, Yi Xu, Weijie Yu, Chenglei Shen, Ming He, Jianping Fan, Xiao Zhang, and Jun Xu. 2025 · 2025
Closest in time.
Unified generative search and recommendation
Teng Shi, Jun Xu, Xiao Zhang, Xiaoxue Zang, Kai Zheng, Yang Song, and Enyun Yu. 2025 · 2025
Closest in time.
Cite before you speak: Enhancing context-response grounding in e-commerce conversational llm-agents
Jingying Zeng, Hui Liu, Zhenwei Dai, Xianfeng Tang, Chen Luo, Samarth Varshney, Zhen Li, and Qi He. 2025 · 2025
Closest in time.
Saqrec: Aligning recommender systems to user satisfaction via questionnaire feedback
Kepu Zhang, Teng Shi, Sunhao Dai, Xiao Zhang, Yinfeng Li, Jing Lu, Xiaoxue Zang, Yang Song, and Jun Xu. 2024c · 2025
Closest in time.