A Survey Paper on Recommender Systems <s> Background <s> Automated collaborative filtering (ACF) systems predict a person's affinity for items or information by connecting that person's recorded interests with the recorded interests of a community of people and sharing ratings between like-minded persons. However, current recommender systems are black boxes, providing no transparency into the working of the recommendation. Explanations provide that transparency, exposing the reasoning and data behind a recommendation. In this paper, we address explanation interfaces for ACF systems - how they should be implemented and why they should be implemented. To explore how, we present a model for explanations based on the user's conceptual model of the recommendation process. We then present experimental results demonstrating what components of an explanation are the most compelling. To address why, we present experimental evidence that shows that providing explanations can improve the acceptance of ACF systems. We also describe some initial explorations into measuring how explanations can improve the filtering performance of users. <s> BIB001 </s> A Survey Paper on Recommender Systems <s> Background <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 </s> A Survey Paper on Recommender Systems <s> Background <s> This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user's interests. Content-based recommendation systems may be used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale. Although the details of various systems differ, content-based recommendation systems share in common a means for describing the items that may be recommended, a means for creating a profile of the user that describes the types of items the user likes, and a means of comparing items to the user profile to determine what to recommend. The profile is often created and updated automatically in response to feedback on the desirability of items that have been presented to the user. <s> BIB003
Recommender system can be built with many approaches. Below are some of them: • Random prediction algorithm is an algorithm that randomly chooses items from the set of available items and recommends them to the user. Since the item's selection is done randomly, the accuracy of the algorithm is based on luck; the greater the number of items is, the chance of good selection lowers. Random prediction has a great probability of failure. Thus, it has never been taken seriously by any researcher or vendor and only serves as reference point 1 , helping to compare the quality of the results obtained by the utilization of a more sophisticated algorithm BIB002 . • Frequent sequences can help build recommender systems. For example, if a customer frequently rates items we can use the frequent pattern to recommend other items to him. The only problem is that this method will only be efficient after the customer makes minimum purchases. • Collaborative filtering algorithms (CF ) are algorithms that require the recommendation seekers to express their preferences by rating items. In this algorithm, the roles of recommendation seeker (a user) and preference provider 2 are merged; the more users rate items (or categories), the more accurate the recommendation becomes. In most CF approaches, there is a list of users U = u1, u2, . . . , um and a list of items I = i1, i2, . . . , in. Each user ui has a list of item Iu i on which he has expressed his opinion  . • Content based algorithms are algorithms that attempt to recommend items that are similar to items the user liked in the past. They treat the recommendation's problem as a search for related items. Information about each item is stored and used for the recommendations. Items selected for recommendation are items that content correlates the most with the user's preferences  . For example, whenever a user rated an items, the algorithm constructs a search query to find other popular items by the same author, artist, or director, or with similar keywords or subjects  . Content based algorithms analyze item descriptions to identify items that are of particular interest to the user BIB003 . Many others approaches for recommender system exist. However, collaborative filtering algorithms have come to be the best of recommendation algorithms. As stated by Papagelis, collaborative filtering algorithms have " been extensively adopted by both research and e-commerce recommendation systems in order to provide an intelligent mechanism to filter out the excess of information available and to provide customers with the prospect to effortlessly find out items that they will probably like according to their logged history of prior transactions" BIB002 . CF algorithms have significant advantages over traditional content-based filtering; they can filter any type of content, e.g. text, artwork, music, mutual funds. They can also filter based on complex and hard to represent concepts such as taste and quality. They can to make serendipitous 3 recommendations. CF algorithms do not depend on error-prone machine analysis of content BIB001 .