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.
Nowadays, recommender systems are widely used in many areas as a solution to deal with information overload. There are some popular and effective methods to build a good recommendation system: collaborative filtering, content-based, knowledge-based and hybrid. Another approach, which made a significant progress over the last several years, are context-aware recommenders. There are many additional information related to the context or application area of recommender systems, which can be useful to generate accurate propositions, e.g. user localisation, items categories or attributes, a day of a week or time of a day, weather. Another issue is recommenders evaluation. Usually, they are only assessed with respect to their prediction accuracy (RMSE, MAE). This is good solution, due to possibility of off-line calculation. However, in real environment recommendation lists are finally evaluated by users who take into consideration many various factors, like novelty or diversity of items. In this article a multi-module collaborative filtering recommender system with consideration of context information is presented. The context is included both in post-filtering module as well as in a similarity measure. Evaluation was made off-line with respect to prediction accuracy and on-line, on real shopping platform.
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