A survey of deep reinforcement learning in recommender systems: A systematic review and future directions
In light of the emergence of deep reinforcement learning (DRL) in recommender systems
research and several fruitful results in recent years, this survey aims to provide a timely and …
research and several fruitful results in recent years, this survey aims to provide a timely and …
[HTML][HTML] Deep reinforcement learning in recommender systems: A survey and new perspectives
In light of the emergence of deep reinforcement learning (DRL) in recommender systems
research and several fruitful results in recent years, this survey aims to provide a timely and …
research and several fruitful results in recent years, this survey aims to provide a timely and …
Relevance Feedback with Brain Signals
The Relevance Feedback (RF) process relies on accurate and real-time relevance
estimation of feedback documents to improve retrieval performance. Since collecting explicit …
estimation of feedback documents to improve retrieval performance. Since collecting explicit …
State of the Art of User Simulation approaches for conversational information retrieval
Conversational Information Retrieval (CIR) is an emerging field of Information Retrieval (IR)
at the intersection of interactive IR and dialogue systems for open domain information …
at the intersection of interactive IR and dialogue systems for open domain information …
A relative information gain-based query performance prediction framework with generated query variants
Query performance prediction (QPP) methods, which aim to predict the performance of a
query, often rely on evidences in the form of different characteristic patterns in the …
query, often rely on evidences in the form of different characteristic patterns in the …
User retention-oriented recommendation with decision transformer
Improving user retention with reinforcement learning (RL) has attracted increasing attention
due to its significant importance in boosting user engagement. However, training the RL …
due to its significant importance in boosting user engagement. However, training the RL …
Contextualized query expansion via unsupervised chunk selection for text retrieval
When ranking a list of documents relative to a given query, the vocabulary mismatches could
compromise the performance, as a result of the different language used in the queries and …
compromise the performance, as a result of the different language used in the queries and …
Boosting search engines with interactive agents
This paper presents first successful steps in designing search agents that learn meta-
strategies for iterative query refinement in information-seeking tasks. Our approach uses …
strategies for iterative query refinement in information-seeking tasks. Our approach uses …
A survey on the memory mechanism of large language model based agents
Large language model (LLM) based agents have recently attracted much attention from the
research and industry communities. Compared with original LLMs, LLM-based agents are …
research and industry communities. Compared with original LLMs, LLM-based agents are …
Generative query reformulation for effective adhoc search
Performing automatic reformulations of a user's query is a popular paradigm used in
information retrieval (IR) for improving effectiveness--as exemplified by the pseudo …
information retrieval (IR) for improving effectiveness--as exemplified by the pseudo …