<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p><p>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.</p></abstract>

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.

Clustering Algorithms in Hybrid Recommender System on MovieLens Data

Department of Information Systems and Computer Networks, Faculty of Computer Science

Studies in Logic, Grammar and Rhetoric

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Clustering Algorithms in Hybrid Recommender System on MovieLens Data"}

Decisions are taken by humans very often during professional as well as leisure activities. It is particularly evident during surfing the Internet: selecting...

Clustering Algorithms in Hybrid Recommender System on MovieLens Data

Data publikacji: 08 sie 2014

Zakres stron: 125 - 139

DOI: https://doi.org/10.2478/slgr-2014-0021

Słowa kluczowe
recommender system, support of decision making, clustering

© 2014 Studies in Logic, Grammar and Rhetoric

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Clustering Algorithms in Hybrid Recommender System on MovieLens Data

Data publikacji: 08 sie 2014

Zakres stron: 125 - 139

DOI: https://doi.org/10.2478/slgr-2014-0021

Słowa kluczowerecommender system, support of decision making, clustering

© 2014 Studies in Logic, Grammar and Rhetoric

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
recommender system, support of decision making, clustering