Research on entropy-based collaborative filtering algorithm and personalized recommendation in e-commerce

Based on the introduction to the user-based and item-based collaborative filtering algorithms, the problems related to the two algorithms are analyzed, and a new entropy-based recommendation algorithm is proposed. Aiming at the drawbacks of traditional similarity measurement methods, we put forward an improved similarity measurement method. The entropy-based collaborative filtering algorithm contributes to solving the cold-start problem and discovering users’ hidden interests. Using the data selected from Movielens and Book-Crossing datasets and MAE accuracy metric, three different collaborative filtering recommendation algorithms are compared through experiments. The experimental scheme and results are discussed in detail. The results show that the entropy-based algorithm provides better recommendation quality than user-based algorithm and achieves recommendation accuracy comparable to the item-based algorithm. At last, a solution to B2B e-commerce recommendation applications based on Web services technology is proposed, which adopts entropy-based collaborative filtering recommendation algorithm.     

Textual case-based reasoning for spam filtering: a comparison of feature-based and feature-free approaches

Spam filtering is a text classification task to which Case-Based Reasoning (CBR) has been successfully applied. We describe the ECUE system, which classifies emails using a feature-based form of textual CBR. Then, we describe an alternative way to compute the distances between cases in a feature-free fashion, using a distance measure based on text compression. This distance measure has the advantages of having no set-up costs and being resilient to concept drift. We report an empirical comparison, which shows the feature-free approach to be more accurate than the feature-based system. These results are fairly robust over different compression algorithms in that we find that the accuracy when using a Lempel-Ziv compressor (GZip) is approximately the same as when using a statistical compressor (PPM). We note, however, that the feature-free systems take much longer to classify emails than the feature-based system. Improvements in the classification time of both kinds of systems can be obtained by applying case base editing algorithms, which aim to remove noisy and redundant cases from a case base while maintaining, or even improving, generalisation accuracy. We report empirical results using the Competence-Based Editing (CBE) technique. We show that CBE removes more cases when we use the distance measure based on text compression (without significant changes in generalisation accuracy) than it does when we use the feature-based approach.     

Tracing the development of teacher knowledge in a design seminar: Integrating content,pedagogy and technology

Effective technology integration for teaching subject matter requires knowledge not just of content, technology and pedagogy, but also of their relationship to each other. Building on Schulman’s [Schulman, L. S. (1987). Knowledge and teaching: foundations for a new reform. Harvard Educational Review, 57(1), 1–22] concept of pedagogical content knowledge, we introduce a framework for conceptualizing Technological Pedagogical Content Knowledge—TPCK [Mishra, P., Koehler, M.J., (in press). Technological pedagogical content knowledge: A new framework for teacher knowledge. Teachers College Record]. We report the results of a semester-long investigation of the development of TPCK during a faculty development design seminar, whereby faculty members worked together with masters students to develop online courses. Quantitative discourse analysis of 15 weeks of field notes for two of the design teams show participants moved from considering technology, pedagogy and content as being independent constructs towards a richer conception that emphasized connections among the three knowledge bases. Our analyses suggests that developing TPCK is a multigenerational process, involving the development of deeper understandings of the complex web of relationships between content, pedagogy and technology and the contexts in which they function. Pedagogic, pragmatic, theoretical, and methodological contributions are discussed.