Effects of binary similarity measures on collaborative filtering

With increasing popularity of the Internet, shopping over the Internet through several online vendors is also receiving increasing attention. Customers want to purchase the appropriate products. In other words, they try to select those products that they might like. In order to help their customers, many online companies utilize collaborative filtering systems. Such systems provide two services, namely prediction and top-N recommendations. Quality of these two services mainly depends on similarity measures that collaborative filtering algorithms use in order to determine the most similar entities. Data collected for collaborative filtering purposes might include either numeric or binary ratings. Several studies have been conducted to compare different similarity measures proposed for numeric data. Although there are various binary ratings-based similarity metrics, their effects on accuracy and performance in collaborative filtering systems have not been deeply studied.In this thesis, we investigate seven binary ratings-based similarity metrics in terms of both accuracy and online performance while providing predictions for single items and top-N lists. Although there are more than seven measures, we consider the most widely used ones in various data mining applications. To compare them in terms of correctness and efficiency, we perform several experiments based on two well-known real data sets. We produce both predictions and top-N lists while using different similarity metrics, where we propose to modify prediction and top-N recommendation algorithms in such a way so that the most similar users? data are involved in collaborative filtering process. We also study how varying controlling parameters affect overall performance with different similarity metrics. We analyze our empirical results in terms of preciseness and performance.Keywords: Similarity measures, prediction, top-N recommendation, accuracy, performance.