Recommender or Recommendation Systems (RS) aim to help users dealing with information overload: finding relevant items in a vast space of resources. Research on RS has been active since the development of the first recommender system in the early 1990s, Tapestry, and some articles and books that survey algorithms and application domains have been published recently. However, these surveys have not extensively covered the different types of information used in RS (sources of knowledge), and only a few of them have reviewed the different ways to assess the quality and performance of RS. In order to bridge this gap, in this chapter we present a classification of recommender systems, and then we focus on presenting the main sources of knowledge and evaluation metrics that have been described in the research literature.

Abstract. Most of the research studies on recommender systems are focused on single-domain recommendations. With the growth of multidomain internet stores such as iTunes, Google Play, and Amazon.com, an opportunity to offer recommendations across different domains become more and more attractive. But there are few research studies on cross-domain recommender systems. In this paper, we study both the cold-start problem and the hypothesis that cross-domain recommendations provide more accuracy using a large volume of user data from a true multi-domain recommender service. Our results indicate that crossdomain collaborative filtering could significantly improve the quality of recommendation in cold start context and the auxiliary profile size plays an important role in it.

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Abstract-Social navigation and social tagging technologies enable user communities to assemble the collective wisdom, and use it to help community members in finding the right information. However, it takes a significantly-sized community to make a social system truly useful. The question addressed in this paper is whether collaborative information finding is feasible in the context of smaller communities. To answer this question, we developed two social systems specifically focused on smaller communities -CoMeT and Conference Navigator II -and explored several techniques to increase the volume of user contributions. This paper reviews the explored techniques and presents empirical evidence that demonstrate their effectiveness.

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Abstract. In this paper, we compare pioneer methods of educational data mining field with recommender systems techniques for predicting student performance. Additionally, we study the importance of including students' attempt time sequences of parameterized exercises. The approaches we use are Bayesian Knowledge Tracing (BKT), Performance Factor Analysis (PFA), Bayesian Probabilistic Tensor Factorization (BPTF), and Bayesian Probabilistic Matrix Factorization (BPMF). The last two approaches are from the recommender system's field. We approach the problem using question-level Knowledge Components (KCs) and test the methods using cross-validation. In this work, we focus on predicting students' performance in parameterized exercises. Our experiments shows that advanced recommender system techniques are as accurate as the pioneer methods in predicting student performance. Also, our studies show the importance of considering time sequence of students' attempts to achieve the desirable accuracy.