A Survey Paper on Recommender Systems <s> Similarity <s> The Internet and World Wide Web have brought us into a world of endless possibilities: interactive Web sites to experience, music to listen to, conversations to participate in, and every conceivable consumer item to order. But this world also is one of endless choice: how can we select from a huge universe of items of widely varying quality? Computational recommender systems have emerged to address this issue. They enable people to share their opinions and benefit from each other’s experience. We present a framework for understanding recommender systems and survey a number of distinct approaches in terms of this framework. We also suggest two main research challenges: (1) helping people form communities of interest while respecting personal privacy, and (2) developing algorithms that combine multiple types of information to compute recommendations. <s> BIB001 </s> A Survey Paper on Recommender Systems <s> Similarity <s> Recommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given resource. There are three main types of recommender systems: collaborative filtering, content-based filtering, and demographic recommender systems. Collaborative filtering recommender systems recommend items by taking into account the taste (in terms of preferences of items) of users, under the assumption that users will be interested in items that users similar to them have rated highly. Content-based filtering recommender systems recommend items based on the textual information of an item, under the assumption that users will like similar items to the ones they liked before. Demographic recommender systems categorize users or items based on their personal attribute and make recommendation based on demographic categorizations. These systems suffer from scalability, data sparsity, and cold-start problems resulting in poor quality recommendations and reduced coverage. In this paper, we propose a unique cascading hybrid recommendation approach by combining the rating, feature, and demographic information about items. We empirically show that our approach outperforms the state of the art recommender system algorithms, and eliminates recorded problems with recommender systems. <s> BIB002 </s> A Survey Paper on Recommender Systems <s> Similarity <s> Understand the need for analyses of large, complex, information-rich data sets. Identify the goals and primary tasks of the data-mining process. Describe the roots of data-mining technology. Recognize the iterative character of a data-mining process and specify its basic steps. Explain the influence of data quality on a data-mining process. Establish the relation between data warehousing and data mining. Data mining is an iterative process within which progress is defined by discovery, through either automatic or manual methods. Data mining is most useful in an exploratory analysis scenario in which there are no predetermined notions about what will constitute an "interesting" outcome. Data mining is the search for new, valuable, and nontrivial information in large volumes of data. It is a cooperative effort of humans and computers. Best results are achieved by balancing the knowledge of human experts in describing problems and goals with the search capabilities of computers. In practice, the two primary goals of data mining tend to be prediction and description. Prediction involves using some variables or fields in the data set to predict unknown or future values of other variables of interest. Description, on the other hand, focuses on finding patterns describing the data that can be interpreted by humans. Therefore, it is possible to put data-mining activities into one of two categories: Predictive data mining, which produces the model of the system described by the given data set, or Descriptive data mining, which produces new, nontrivial information based on the available data set. <s> BIB003
Similarity (closeness) is define by data analysis in a term of a distance 7 function such as the Euclidean (Equation 1) and the Manhattan (Equation 2). In those distance functions, the difference between corresponding values of attributes in tuples i and j are taken. BIB002 Tapestry was the first recommendation support system to be made. It was build at Xerox R Parc which also famous for inventing graphical operating system BIB001 BIB003 . Some of those methods are cosinebased similarity, Conditional Probability-Based Similarity and Pearson correlation Similarity.