Research on Community Knowledge Modeling of Readers Based on Interest Labels
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초록
Community portraits can deeply explore the characteristics of community structures and describe the personalizedknowledge needs of community users, which is of great practical significance for improving communityrecommendation services, as well as the accuracy of resource push. The current community portraits generallyhave the problems of weak perception of interest characteristics and low degree of integration of topic information. To resolve this problem, the reader community portrait method based on the thematic and timelinesscharacteristics of interest labels (UIT) is proposed. First, community opinion leaders are identified based onmulti-feature calculations, and then the topic features of their texts are identified based on the LDA topic model. On this basis, a semantic mapping including “reader community-opinion leader-text content” was established. Second, the readers' interest similarity of the labels was dynamically updated, and two kinds of tag parameterswere integrated, namely, the intensity of interest labels and the stability of interest labels. Finally, the similaritydistance between the opinion leader and the topic of interest was calculated to obtain the dynamic interest setof the opinion leaders. Experimental analysis was conducted on real data from the Douban reading community. The experimental results show that the UIT has the highest average F value (0.551) compared to the state-ofthe-art approaches, which indicates that the UIT has better performance in the smooth time dimension.
Abstract
Community portraits can deeply explore the characteristics of community structures and describe the personalizedknowledge needs of community users, which is of great practical significance for improving communityrecommendation services, as well as the accuracy of resource push. The current community portraits generallyhave the problems of weak perception of interest characteristics and low degree of integration of topic information. To resolve this problem, the reader community portrait method based on the thematic and timelinesscharacteristics of interest labels (UIT) is proposed. First, community opinion leaders are identified based onmulti-feature calculations, and then the topic features of their texts are identified based on the LDA topic model. On this basis, a semantic mapping including “reader community-opinion leader-text content” was established. Second, the readers' interest similarity of the labels was dynamically updated, and two kinds of tag parameterswere integrated, namely, the intensity of interest labels and the stability of interest labels. Finally, the similaritydistance between the opinion leader and the topic of interest was calculated to obtain the dynamic interest setof the opinion leaders. Experimental analysis was conducted on real data from the Douban reading community. The experimental results show that the UIT has the highest average F value (0.551) compared to the state-ofthe-art approaches, which indicates that the UIT has better performance in the smooth time dimension.
참고문헌 (16)
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