3 research outputs found

    Explainable Recommendation: Theory and Applications

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    Although personalized recommendation has been investigated for decades, the wide adoption of Latent Factor Models (LFM) has made the explainability of recommendations a critical issue to both the research community and practical application of recommender systems. For example, in many practical systems the algorithm just provides a personalized item recommendation list to the users, without persuasive personalized explanation about why such an item is recommended while another is not. Unexplainable recommendations introduce negative effects to the trustworthiness of recommender systems, and thus affect the effectiveness of recommendation engines. In this work, we investigate explainable recommendation in aspects of data explainability, model explainability, and result explainability, and the main contributions are as follows: 1. Data Explainability: We propose Localized Matrix Factorization (LMF) framework based Bordered Block Diagonal Form (BBDF) matrices, and further applied this technique for parallelized matrix factorization. 2. Model Explainability: We propose Explicit Factor Models (EFM) based on phrase-level sentiment analysis, as well as dynamic user preference modeling based on time series analysis. In this work, we extract product features and user opinions towards different features from large-scale user textual reviews based on phrase-level sentiment analysis techniques, and introduce the EFM approach for explainable model learning and recommendation. 3. Economic Explainability: We propose the Total Surplus Maximization (TSM) framework for personalized recommendation, as well as the model specification in different types of online applications. Based on basic economic concepts, we provide the definitions of utility, cost, and surplus in the application scenario of Web services, and propose the general framework of web total surplus calculation and maximization.Comment: 169 pages, in Chinese, 3 main research chapter

    Concept Mapping Strategy For Academic Writing Tutorial In Open And Distant Learning Higher Institution

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    Universitas Terbuka (UT) an open and distant higher education institution of Indonesia conducts the in-service teacher education program. In order to complete the program, the students – mostly teachers - have to submit the final academic paper. In fact, most of the UT students have difficulty to write this academic paper. UT offers an academic writing course to solve this writing program. Most of the student view academic writing still as a difficult assignment. Most of the students view academic writing as a difficult assignment to complete. UT has to find an appropriate instructional strategy that can facilitate student to write the academic writing assignment. One of the instructional strategy that can be selected to solve the academic writing problems is concept mapping. The aim of this study is to elaborate the implementation of concept map as an instructional strategy to facilitate the open and distance learning students io complete academic writing assignments. A design based research was applied to measure the effectiveness of using concept mapping strategy in helping students to gain academic writing skills. The steps of research and development model from Borg, Gall and Gall which consist of instructional design and development phases were implemented in this study. The result of this study indicated that students were facilitated and enjoyed the process of academic writing used the concept map strategy
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