A Survey Paper on Recommender Systems <s> M. Papagelisa and D. Plexousakis define a Pearson <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> M. Papagelisa and D. Plexousakis define a Pearson <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> M. Papagelisa and D. Plexousakis define a Pearson <s> Collaborative Filtering (CF), the prevalent recommendation approach, has been successfully used to identify users that can be characterized as “similar” according to their logged history of prior transactions. However, the applicability of CF is limited due to the sparsity problem, which refers to a situation that transactional data are lacking or are insufficient. In an attempt to provide high-quality recommendations even when data are sparse, we propose a method for alleviating sparsity using trust inferences. Trust inferences are transitive associations between users in the context of an underlying social network and are valuable sources of additional information that help dealing with the sparsity and the cold-start problems. A trust computational model has been developed that permits to define the subjective notion of trust by applying confidence and uncertainty properties to network associations. We compare our method with the classic CF that does not consider any transitive associations. Our experimental results indicate that our method of trust inferences significantly improves the quality performance of the classic CF method. <s> BIB003
Correlation function for a recommender where the item rating is captured by looking at the explicit rating the users gave to the categories. Let C = cx : x = 1, 2, . . . , n ∧ n ≤ n where n is the total number of categories in the database that users ux and uy have both rated. The similarity between ux and uy is given by BIB002 : where cx; x = 1, 2, . . . , p are the available categories. After users have been clustered, the algorithms pursue by finding popular items between those users and recommend them  . Although user based algorithms are very efficient and give good results, they suffer some drawbacks; • sparsity: The number of users and items in major ecommerce website is very large. Most of the users however only rated a small portion of the total items available; even very popular items result in having been rated by only a few of the total number of users. This means that the user-item matrix is very sparse and has a lot of 0 element. Because of that it is possible that the similarity between two users cannot be defined thus making the algorithm useless BIB003 . Even when the evaluation of similarity is possible, it may not be very reliable, because of insufficient information processed. • scalability: Because finding the optimal clusters of users over large data sets is impractical. Most user-based recommenders use various forms of greedy cluster generation algorithms such as Lazy learner k-nearest neighbors. These cluster generation algorithms require a lot of computations 8 that grow linearly with the numbers of users and cannot be precomputed because users and items are changing over time in the database. Moreover, since the user based algorithms must compute BIB001 For example in the k-nearest neighbors algorithm, finding the optimal k requires a large amount of computations the k-nearest neighbors for every users browsing the system, the latency (waiting time) for each recommendation will increase and may it affect real-time performance of the system  . The conclusion is that userbased algorithm do not scale well and are not suitable for large databases of users and items.