Recommender System Using Reinforcement Learning

منو

" Recommender System Using Reinforcement Learning "
Sargar, Rushikesh Bapu Atkinson, Robert K.;Chen, Yinong

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	1114338
Doc. No	:	TLpq2406982400
Main Entry	:	Atkinson, Robert K.
	:	Sargar, Rushikesh Bapu
Title & Author	:	Recommender System Using Reinforcement Learning\ Sargar, Rushikesh BapuAtkinson, Robert K.;Chen, Yinong
College	:	Arizona State University
Date	:	2020
student score	:	2020
Degree	:	M.S.
Page No	:	82
Abstract	:	Currently, recommender systems are used extensively to find the right audience with the "right" content over various platforms. Recommendations generated by these systems aim to offer relevant items to users. Different approaches have been suggested to solve this problem mainly by using the rating history of the user or by identifying the preferences of similar users. Most of the existing recommendation systems are formulated in an identical fashion, where a model is trained to capture the underlying preferences of users over different kinds of items. Once it is deployed, the model suggests personalized recommendations precisely, and it is assumed that the preferences of users are perfectly reflected by the historical data. However, such user data might be limited in practice, and the characteristics of users may constantly evolve during their intensive interaction between recommendation systems.
Subject	:	Artificial intelligence
	:	Biclustering
	:	Computer science
	:	Qlearning
	:	Recommender system
	:	Reinforcement learning
Added Entry	:	Chen, Yinong