عنوان

Recommender System Using Reinforcement Learning

Number

TLpq2406982400

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

Recommender System Using Reinforcement Learning

General Material Designation

[Thesis]

First Statement of Responsibility

Sargar, Rushikesh Bapu

Subsequent Statement of Responsibility

Atkinson, Robert K.;Chen, Yinong

Name of Publisher, Distributor, etc.

Arizona State University

Date of Publication, Distribution, etc.

2020

Specific Material Designation and Extent of Item

82

Dissertation or thesis details and type of degree

M.S.

Body granting the degree

Arizona State University

Text preceding or following the note

2020

Text of Note

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.

Artificial intelligence

Biclustering

Computer science

Qlearning

Recommender system

Reinforcement learning

Atkinson, Robert K.

Sargar, Rushikesh Bapu

Chen, Yinong

Electronic name

p

[Thesis]

276903

a

Y