Technical Evaluation of Boolean Recommenders
The purpose of this paper is to describe a new methodology dedicated to the analysis of boolean recommenders. The aim of most recommender system is to suggest interesting items to a given user. The most common criteria utilized to evaluate a system are its statistical correctness and completeness. The two can be measured by accuracy and recall indices. In this paper we argue that technical performance is an important step in the process of recommender system’s evaluation. We focus on four real-life characteristics i.e. time required to build a model, memory consumption of the built model, expected latency of creating a recommendation for a random user and finally the time required to retrain the model with new ratings. We adapt a recently developed evaluation technique, which is based on an iterative generation of bipartite graphs. In this paper we concentrate on a case when preferences are boolean, which is opposite to value-based ratings.
KeywordsBipartite Graph Recommender System Preferential Attachment Memory Consumption Collaborative Filter
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