KERL: A knowledge-guided reinforcement learning model for sequential recommendation

P Wang, Y Fan, L Xia, WX Zhao, SZ Niu… - Proceedings of the 43rd …, 2020 - dl.acm.org
For sequential recommendation, it is essential to capture and predict future or long-term user
preference for generating accurate recommendation over time. To improve the predictive …

" Deep reinforcement learning for search, recommendation, and online advertising: a survey" by Xiangyu Zhao, Long Xia, Jiliang Tang, and Dawei Yin with Martin …

X Zhao, L Xia, J Tang, D Yin - ACM sigweb newsletter, 2019 - dl.acm.org
Search, recommendation, and online advertising are the three most important information-
providing mechanisms on the web. These information seeking techniques, satisfying users' …

Neural re-ranking in multi-stage recommender systems: A review

W Liu, Y Xi, J Qin, F Sun, B Chen, W Zhang… - arXiv preprint arXiv …, 2022 - arxiv.org
As the final stage of the multi-stage recommender system (MRS), re-ranking directly affects
user experience and satisfaction by rearranging the input ranking lists, and thereby plays a …

Query generation and buffer mechanism: Towards a better conversational agent for legal case retrieval

B Liu, Y Wu, F Zhang, Y Liu, Z Wang, C Li… - Information Processing …, 2022 - Elsevier
In legal case retrieval, existing work has shown that human-mediated conversational search
can improve users' search experience. In practice, a suitable workflow can provide …

Diversification-aware learning to rank using distributed representation

L Yan, Z Qin, RK Pasumarthi, X Wang… - Proceedings of the Web …, 2021 - dl.acm.org
Existing work on search result diversification typically falls into the “next document”
paradigm, that is, selecting the next document based on the ones already chosen. A …

Reinforcement learning to rank with pairwise policy gradient

J Xu, Z Wei, L Xia, Y Lan, D Yin, X Cheng… - Proceedings of the 43rd …, 2020 - dl.acm.org
This paper concerns reinforcement learning~(RL) of the document ranking models for
information retrieval~(IR). One branch of the RL approaches to ranking formalize the …

Diversifying search results using self-attention network

X Qin, Z Dou, JR Wen - Proceedings of the 29th ACM International …, 2020 - dl.acm.org
Search results returned by search engines need to be diversified in order to satisfy different
information needs of different users. Several supervised learning models have been …

All roads lead to rome: Unveiling the trajectory of recommender systems across the llm era

B Chen, X Dai, H Guo, W Guo, W Liu, Y Liu… - arXiv preprint arXiv …, 2024 - arxiv.org
Recommender systems (RS) are vital for managing information overload and delivering
personalized content, responding to users' diverse information needs. The emergence of …

Rlirank: Learning to rank with reinforcement learning for dynamic search

J Zhou, E Agichtein - Proceedings of The Web Conference 2020, 2020 - dl.acm.org
To support complex search tasks, where the initial information requirements are complex or
may change during the search, a search engine must adapt the information delivery as the …

An in-depth study on adversarial learning-to-rank

HT Yu, R Piryani, A Jatowt, R Inagaki, H Joho… - Information Retrieval …, 2023 - Springer
In light of recent advances in adversarial learning, there has been strong and continuing
interest in exploring how to perform adversarial learning-to-rank. The previous adversarial …