Content Filtering Based on Keyword Map

This paper presents the content filtering based on keyword map. Keyword maps represent knowledge of learner by capturing relations between terms that the learner has been exposed to. Keyword maps allow increasing both the relevance and complement of learning resources recommendation. The experimental results show that the proposed method is better suited for e-learning settings our method, and achieves a higher accuracy than common recommendation methods such as collaborative and keyword based approaches. Introduction Nowadays personalization is regarded as crucial in e-learning settings. Many e-learning systems have applied recommender systems to support learning resources personalization, such as the paper recommendation proposed by Tang et al. [1], the learning activities recommendation developed by Drachsler et al. [2], the learning process recommendation proposed by Wan et al. [3,4,5]. Content-based recommender systems always use keyword vectors to carry out recommendations. The process of content-based recommender systems is as follows: (1) identifying the item contents, (2) identifying the user’s interests and (3) using the methods used to match them. Therefore, in order to design the accuracy content-based recommender system, it is necessary to consider the following problems, (1) how to represent a content of items (content profiles), (2) how to create user profiles that stand for users’ preferences. In this paper, we propose keyword map based recommendation method. Keyword maps represent knowledge of learner by capturing relations between terms that the learner has been exposed to. Keyword maps are constructed based on the learner’s user profile, which consists of visited learning resources, and learning processes in a web based learning system. Approach In e-learning settings, keyword vector-based methods are not necessarily effective, since recommendation of learning resources depends not only on the keywords, but also on existing knowledge of learner and its relations to a resource considered for recommendation (e.g. appropriate difficulty, subject area, course plan, etc.). To overcome these limitations we propose keyword map based recommendation method [1]. The approach utilize keyword map to provide a mechanism for representing the information of text. Each keyword in a keyword map is used to represent a subject or object of a sentence. Keyword maps are constructed based on the learner’s user profile, which consists of visited learning resources, and learning processes in a web based learning system. Content filtering based on keyword map (Fig.1, Fig.2, Fig.3) consists of two parts: (1) generating learner’s keyword map based profile and (2) Jaccard recommendation. Using keyword map on the construction of user profiles: the system creates learners’ keyword map based profiles automatically based on their learning processes implicitly (Tab.1). Jaccard recommendation: we use a fuzzy version of the standard Jaccard similarity measure between the keyword map of learner profile and keyword map of each learning resources to decide the recommendation. 2nd International Conference on Electrical, Computer Engineering and Electronics (ICECEE 2015) © 2015. The authors Published by Atlantis Press 851 Fig.1 Example of keyword map based learner profile (in database) This is User b004’s keyword map based learner profile. Fig.2 Example of keyword map based learner profile (Visualization) This is the real keyword map profile of learner “b004” (keyword: 388; relation: 574). The visualization is based on Gephi (www.gephi.org). The layouts of keyword map are (a) OpenOrd; (b) Force Altlas; (c) Yifan Hu; (d) Fruchterman Reingold. The structure of keyword map allows restricting the search space and inferring missing information. The intuition behind content filtering based on keyword map is that the information represent by keyword map can be used to estimate missing preferences.