Cluster based collaborative filtering with inverted indexing
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Collectively, a population contains vast amounts of knowledge and modern communication technologies that increase the ease of communication. However, it is not feasible for a single person to aggregate the knowledge of thousands or millions of data and extract useful information from it. Collaborative information systems are attempts to harness the knowledge of a population and to present it in a simple, fast and fair manner. Collaborative filtering has been successfully used in domains where the information content is not easily parse-able and traditional information filtering techniques are difficult to apply. Collaborative filtering works over a database of ratings for the items which are rated by users. The computational complexity of these methods grows linearly with the number of customers which can reach to several millions in typical commercial applications. To address the scalability concern, we have developed an efficient collaborative filtering technique by applying user clustering and using a specific inverted index structure (so called cluster-skipping inverted index structure) that is tailored for clustered environments. We show that the predictive accuracy of the system is comparable with the collaborative filtering algorithms without clustering, whereas the efficiency is far more improved.