KERL: A knowledge-guided reinforcement learning model for sequential recommendation
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 …
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 …
Search, recommendation, and online advertising are the three most important information-
providing mechanisms on the web. These information seeking techniques, satisfying users' …
providing mechanisms on the web. These information seeking techniques, satisfying users' …
Neural re-ranking in multi-stage recommender systems: A review
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 …
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
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 …
can improve users' search experience. In practice, a suitable workflow can provide …
Diversification-aware learning to rank using distributed representation
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 …
paradigm, that is, selecting the next document based on the ones already chosen. A …
Reinforcement learning to rank with pairwise policy gradient
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 …
information retrieval~(IR). One branch of the RL approaches to ranking formalize the …
Diversifying search results using self-attention network
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 …
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
Recommender systems (RS) are vital for managing information overload and delivering
personalized content, responding to users' diverse information needs. The emergence of …
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 …
may change during the search, a search engine must adapt the information delivery as the …
An in-depth study on adversarial learning-to-rank
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 …
interest in exploring how to perform adversarial learning-to-rank. The previous adversarial …