http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Xiaofang Ding (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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Community-based Collaborative Filtering Recommendation Algorithm
Xiaofang Ding,Zhixiao Wang,Shaoda Chen,Ying Huang 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.2
Collaborative filtering recommendation technology is by far the most widely used and successful personalized recommendation technology. However, the method currently faced with some problems such as sparse matrix, affecting the accuracy of the predicted results. This paper puts forward a new community detection algorithm based on topological potential theory, and combines it with collaborative filtering recommendation algorithm. The users with similar interests are put into the same community. When searching for the user’s nearest neighbor, it target to the users in a specific community or several communities instead of all users, which narrows the search and improves the prediction accuracy. Experimental results suggest that this approach effectively reduces the impact on the prediction accuracy of the sparse matrix, and significantly improves the prediction ability and recommendation quality.
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An Optimization for Hybrid Semantic Similarity Computation
Zhixiao Wang,Xiaofang Ding,Ying Huang 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.10
Semantic similarity computation is of great importance in many applications such as natural language processing, knowledge acquisition and information retrieval. In recent years, many concept similarity measures have been developed for ontology and lexical taxonomy. Generally speaking, ontology concepts semantic similarity computation is tedious and time-consuming. This paper puts forward an optimization algorithm to simplify semantic similarity computation. The optimization algorithm utilizes hierarchical relationship between concepts to simplify similarity computation process. Simulation experiments showed the optimization algorithm could make similarity computation simple and convenient, and similarity computation speed was improved by one time. The more complexity an ontology structure, and the bigger the maximum depth of ontology, the more significantly the performance improved.
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