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2014 | 37 | 1 | 125-139

Article title

Clustering Algorithms in Hybrid Recommender System on MovieLens Data

Title variants

Languages of publication

EN

Abstracts

EN
Decisions are taken by humans very often during professional as well as leisure activities. It is particularly evident during surfing the Internet: selecting web sites to explore, choosing needed information in search engine results or deciding which product to buy in an on-line store. Recommender systems are electronic applications, the aim of which is to support humans in this decision making process. They are widely used in many applications: adaptive WWW servers, e-learning, music and video preferences, internet stores etc. In on-line solutions, such as e-shops or libraries, the aim of recommendations is to show customers the products which they are probably interested in. As input data the following are taken: shopping basket archives, ratings of the products or servers log files. The article presents a solution of recommender system which helps users to select an interesting product. The system analyses data from other customers' ratings of the products. It uses clustering methods to find similarities among the users and proposed techniques to identify users' profiles. The system was implemented in Apache Mahout environment and tested on a movie database. Selected similarity measures are based on: Euclidean distance, cosine as well as correlation coefficient and loglikehood function.

Publisher

Year

Volume

37

Issue

1

Pages

125-139

Physical description

Dates

online
2014-08-08

Contributors

  • Department of Information Systems and Computer Networks, Faculty of Computer Science, Bialystok University of Technology, Poland

References

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Document Type

Publication order reference

Identifiers

YADDA identifier

bwmeta1.element.doi-10_2478_slgr-2014-0021
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