Semantic-Based Recommendation Method for Sport News Aggregation System

Abstract : News on the Internet today plays an important role in helping people access daily information around the world. News aggregators are websites that collect and provide content from different sources in one location for easy viewing. However, the increasing number of news on the Internet makes it difficult for readers when they desire to access news they are concerned. One solution to this issue is based on employing recommender systems. In this research, we propose a novel method for news recommendation based on a combination of semantic similarity with content similarity between news and implement it as a feature of semantic-based news aggregators BKSport. Experimental results have shown that, a combination of both kind of similarity measures will result in better recommendation than when using either measure separately.
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Communication dans un congrès
A Min Tjoa; Li Da Xu; Maria Raffai; Niina Maarit Novak. 10th International Conference on Research and Practical Issues of Enterprise Information Systems (CONFENIS), Dec 2016, Vienna, Austria. Springer International Publishing, Lecture Notes in Business Information Processing, LNBIP-268, pp.32-47, 2016, Research and Practical Issues of Enterprise Information Systems. 〈10.1007/978-3-319-49944-4_3〉
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Soumis le : mardi 7 novembre 2017 - 17:27:10
Dernière modification le : jeudi 9 novembre 2017 - 01:16:28
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Quang-Minh Nguyen, Thanh-Tam Nguyen, Tuan-Dung Cao. Semantic-Based Recommendation Method for Sport News Aggregation System. A Min Tjoa; Li Da Xu; Maria Raffai; Niina Maarit Novak. 10th International Conference on Research and Practical Issues of Enterprise Information Systems (CONFENIS), Dec 2016, Vienna, Austria. Springer International Publishing, Lecture Notes in Business Information Processing, LNBIP-268, pp.32-47, 2016, Research and Practical Issues of Enterprise Information Systems. 〈10.1007/978-3-319-49944-4_3〉. 〈hal-01630538〉

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