Usage-based web recommendations: a reinforcement learning approach

By Nima Taghipour, Ahmad Agha Kardan, Saeed Shiry Ghidary. 2007

108 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

DRN: A Deep Reinforcement Learning Framework for News Recommendation (2018, methodology): “…different from the MAB group of methods [5, 7, 23, 40, 44, 50] due to its explicit modeling of future rewards, and different from previous MDP methods [27, 35, 36, 38] using user log due to its continuous representation of state and action, and the capability to scale to large systems.”
Collaborative filtering recommender systems (2013, unknown): “Also, the behavioral pattern data, for example, site browsing patterns in a Web-based recommender system (Taghipour et al ., 2007) or travel search patterns in a travel recommender system (Mahmood and Ricci, 2009) can be used to describe the users.”
Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning (2018, background): “sponses of users in a conversational recommender, with the aim to maximize a numerical cumulative reward function modeling the benefit that users get from each recommendation session. Taghipour et al.[27, 28] modeled web page recommendation as a Q-Learning problem and learned to make recommendations”
Recommender Systems: Introduction and Challenges (2015, background): “Users can also be described by their behavior pattern data, for example, site browsing patterns (in a Web-based recommender system) [54], or travel search patterns (in a travel recommender system) [35].”
Recommender Systems (2016, unknown): “Although reinforcement learning has been studied extensively in the classification and regression modeling literature [579], the corresponding work in the recommendation domain is rather limited [389, 390, 585].”
Introduction to Recommender Systems Handbook (2011, background): “Users can also be described by their behavior pattern data, for example, site browsing patterns (in a Web-based recommender system) [107], or travel search”
Reinforcement Learning based Recommender Systems: A Survey (2021, methodology): “To manage the state space, similar to [57], an N-gram (sliding window) model is used to model the states, i.”
Deep Reinforcement Learning for List-wise Recommendations (2017, background): “Taghipour et al.[25, 26] modeled web page recommendation as a Q-Learning problem and learned to make recommendations from web usage data as the actions rather than discovering explicit patterns from the data.”