Title

Biclustering ARTMAP Collaborative Filtering Recommender System

Abstract

Collaborative filtering provides recommendations based on the behavior of each user combined with behavior of users with similar interests. Recommender systems are becoming widespread, helping people choose movies, books, and things to buy. In this study, we examine the use of Biclustering ARTMAP to build a collaborative filtering recommendation system. We introduce a novel modification to how the Biclustering ARTMAP algorithm computes the item-cluster similarity, and a way to adapt it for the prediction of user ratings. We apply the algorithm to the MovieLens 100k dataset, and find that it achieves promising performance compared to other collaborative filtering techniques.

Meeting Name

2016 International Joint Conference on Neural Networks, IJCNN 2016 (2016: Jul. 24-29, Vancouver, Canada)

Department(s)

Electrical and Computer Engineering

Research Center/Lab(s)

Center for High Performance Computing Research

Keywords and Phrases

Behavioral Research; Recommender Systems; Bi-Clustering; Collaborative Filtering Recommendations; Collaborative Filtering Recommender Systems; Collaborative Filtering Techniques; Item Clusters; Movielens; Similar Interests; User Rating; Collaborative Filtering

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.


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