Hong Kong Polytechnic University

About: Hong Kong Polytechnic University is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: Computer science & Tourism. The organization has 29633 authors who have published 72136 publications receiving 1956312 citations. The organization is also known as: HKPU & PolyU.

BAS: Beetle Antennae Search Algorithm for Optimization Problems

Abstract: Meta-heuristic algorithms have become very popular because of powerful performance on the optimization problem. A new algorithm called beetle antennae search algorithm (BAS) is proposed in the paper inspired by the searching behavior of longhorn beetles. The BAS algorithm imitates the function of antennae and the random walking mechanism of beetles in nature, and then two main steps of detecting and searching are implemented. Finally, the algorithm is benchmarked on 2 well-known test functions, in which the numerical results validate the efficacy of the proposed BAS algorithm.

Smart Recommendation for an Evolving E-Learning System: Architecture and Experiment

Abstract: In this article, we proposed an evolving e-learning system which can adapt itself both to the learners and to the open Web, and we pointed out the differences of making recommendations in e-learning and other domains. We propose two pedagogy features in recommendation: learner interest and background knowledge. A description of a paper's value, similarity, and ordering are presented using formal definitions. We also study two pedagogy-oriented recommendation techniques: content-based and hybrid recommendations. We argue that while it is feasible to apply both of these techniques in our domain, a hybrid collaborative filtering technique is more efficient to make \"just-in-time\" recommendations. In order to assess and compare these two techniques, we carried out an experiment using artificial learners. Experiment results are encouraging, showing that hybrid collaborative filtering, which can lower the computational costs, will not compromise the overall performance of the recommendation system. In addition, as more and more learners participate in the learning process, both learner and paper models can better be enhanced and updated, which is especially desirable for webbased learning systems. We have tested the recommendation mechanisms with real learners, and the results are very encouraging.