Contextual Modelling Collaborative Recommender System—Real Environment Deployment Results

Abstract

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.