A Survey Paper on Recommender Systems <s> Recommendation Metrics <s> Collaborative filtering (CF) has become an important data mining technique to make personalized recommendations for books, web pages or movies, etc. One popular algorithm is the memory-based collaborative filtering, which predicts a user’s preference based on his or her similarity to other users (instances) in the database. However, the tremendous growth of users and the large number of products, memory-based CF algorithms results in the problem of deciding the right instances to use during prediction, in order to reduce executive cost and excessive storage, and possibly to improve the generalization accuracy by avoiding noise and overfitting. In this paper, we focus our work on a typical user preference database that contains many missing values, and propose four novel instance reduction techniques called TURF1-TURF4 as a preprocessing step to improve the efficiency and accuracy of the memory-based CF algorithm. The key idea is to generate prediction from a carefully selected set of relevant instances. We evaluate the techniques on the well-known EachMovie data set. Our experiments showed that the proposed algorithms not just dramatically speed up the prediction, but also improved the accuracy. <s> BIB001 </s> A Survey Paper on Recommender Systems <s> Recommendation Metrics <s> Recommender systems have shown great potential to help users find interesting and relevant items from within a large information space. Most research up to this point has focused on improving the accuracy of recommender systems. We believe that not only has this narrow focus been misguided, but has even been detrimental to the field. The recommendations that are most accurate according to the standard metrics are sometimes not the recommendations that are most useful to users. In this paper, we propose informal arguments that the recommender community should move beyond the conventional accuracy metrics and their associated experimental methodologies. We propose new user-centric directions for evaluating recommender systems. <s> BIB002
Items and users are getting increased in systems where recommender systems utilization is crucial. To ensure user satisfaction all the time, algorithms must not work on thousands, but millions of item within reasonable time BIB001 . Therefore, recommender systems must cope with the growth by making the suggestions more accurate, efficient and scalable. Accuracy This is measured by how close the result of a recommendation matches a user's preference. Accuracy is the most important metric in evaluating the quality if a recommender system because this is what all is about: understanding the user and suggesting what he really likes or what he is looking for precisely to gain the user's trust BIB001 . There are two measures for evaluating the accuracy of a recommender system: • Statistical Accuracy Metric: This compares the numerical recommendation scores against the actual user rating. One of the widely used metrics is the Mean Absolute Error (MAE); the lower the value of MAE the more accurate the result is  . • Decision Support Accuracy: which measure how effective the prediction engine is at helping a user selecting high-quality item from the set of all items. Receiver Operating Characteristic (ROC) is one of the metrics that help assessing the accuracy of predictions  . An interesting point regarding accuracy was pointed out by many researchers. Very accurate recommender systems are not always good! An example is an online travel agency that recommends destinations that has already been visited by a user. Yes the recommendation was accurate enough but it was not useful she already visited these places BIB002 . This means that recommendation must be accurate in predicting the upcoming actions of a user not only knowing him. Moreover, the recommendations that are most accurate according to the standards metrics are sometimes not the recommendations that are useful to users BIB002 .