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As the heterogeneity of data sources are increasing on the web, and due to the sparsity of data in each of these data sources, cross-domain recommendation is becoming an emerging research topic in the recent years. Cross-domain collaborative filtering aims to transfer the user rating pattern from source (auxiliary) domains to a target domain for the purpose of alleviating the sparsity problem and providing better target recommendations. However, the studies so far have either focused on a limited number of domains that are assumed to be related to each other (such as books and movies), or a division of the same dataset (such as movies) into different domains based on an item characteristic (such as genre). In this paper, we study a broad set of domains and their characteristics to understand the factors that affect the success or failure of cross-domain collaborative filtering, the amount of improvement in cross-domain approaches, and the selection of best source domains for a speficic target domain. We propose to use Canonical Correlation Analysis (CCA) as a significant major factor in finding the most promising source domains for a target domain, and suggest a cross-domain collaborative filtering based on CCA (CD-CCA) that proves to be successful in using the shared information between domains in the target recommendations.

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In this paper, we study the effect of using domain knowledge structure on predicting student performance with parameterized Java programming exercises. Domain knowledge structure defines connections between elementary knowledge items. While known to be beneficial in general, it has not been used to predict performance.We compare five different approaches for this purpose: Bayesian Knowledge Tracing (BKT), Performance Factor Analysis (PFA), and three dimensional Bayesian Probabilistic Tensor Factorization (3D-BPTF), that are not able to take into account knowledge structure; and four-dimensional Bayesian Probabilistic Tensor Factorization (4D-BPTF) and Feature-Aware Student Knowledge Tracing (FAST), that can take into account knowledge structure. We approach the problem using both topic-level and question-level Knowledge Components (KCs) and test the methods on a dataset of parameterized questions. Our work is the first in the field that models students’ behavior in a four dimensional tensor. Our experiments show that, when having only the knowledge-item-level information, all of the models work similarly in predicting student performance, but adding the topic-level information that integrates knowledge items changes the performance of these models in different directions.

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