Random graphs for performance evaluation of recommender systems

• Autorzy: Chojnacki Sz. , Kłopotek M. A.
• Języki publikacji: EN

Abstrakty

EN

The purpose of this article is to introduce a new analytical framework dedicated to measuring performance of recommender systems. A standard approach is to assess the quality of a system by means of accuracy related statistics. However, the specificity of the environments in which recommender systems are deployed requires paying much attention to speed and memory requirements of the algorithms. Unfortunately, it is implausible to assess accurately the complexity of various algorithms with formal tools. This can be attributed to the fact that such analyses are usually based on an assumption of dense representation of underlying data structures. In real life, though, the algorithms operate on sparse data and are implemented with collections dedicated for them. Therefore, we propose to measure the complexity of recommender systems with artificial datasets that posses real-life properties. We utilize a recently developed bipartite graph generator to evaluate how the state-of-art recommender system behavior is determined and diversified by topological properties of the generated datasets.

Słowa kluczowe

EN

recommender systems; performance evaluation; random graphs; bipartite complex networks

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2011
• Tom: Vol. 40, no 2
• Strony: 237--257
• Opis fizyczny: Bibliogr. 15 poz., il.

Twórcy

autor

Chojnacki Sz.

autor

Kłopotek M. A.

• Institute of Computer Science, Polish Academy of Sciences Warsaw, Poland

Bibliografia

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