A Survey Paper on Recommender Systems <s> Challenges of Recommender Systems <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> Challenges of Recommender Systems <s> Recommendation agents employ prediction algorithms to provide users with items that match their interests. In this paper, several prediction algorithms are described and evaluated, some of which are novel in that they combine user-based and item-based similarity measures derived from either explicit or implicit ratings. Both statistical and decision-support accuracy metrics of the algorithms are compared against different levels of data sparsity and different operational thresholds. The first metric evaluates the accuracy in terms of average absolute deviation, while the second evaluates how effectively predictions help users to select high-quality items. The experimental results indicate better performance of item-based predictions derived from explicit ratings in relation to both metrics. Category-boosted predictions lead to slightly better predictions when combined with explicit ratings, while implicit ratings, in the context that have been defined in this paper, perform much worse than explicit ratings. <s> BIB002
If recommender systems rely only on items that have been rated, then it is missing a lot of good items for recommendation that are hidden because no one has rated them. This is called the Coverage metrics which is the percentage of items for which a recommender agent can provide predictions BIB002 . This is one of the problems that face systems that employ recommender systems. Another challenge is the sparsity issue which is rating few of the total number of items BIB002 . For systems that has just established, they are facing the cold start problem where the recommender system is unable to accurately recommend items due to the fact that only few rating has been performed on items. Noise, data redundancy, and overfitting are also other challenges of recommendation agent BIB001 . In order to reduce the sparsity problem, some researchers have proposed a compensation system by which users are rewarded for providing ratings to items. Others have proposed to capture the ratings by implicitly look at the user's behavior  . Another approach to solve the sparsity problem is to rely filtering agent called filterbots or dynamic agents to automatically rate items  .