C4.5: programs for machine learning
C4.5: programs for machine learning
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Constructing Web User Profiles: A non-invasive Learning Approach
WEBKDD '99 Revised Papers from the International Workshop on Web Usage Analysis and User Profiling
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
DEXA '07 Proceedings of the 18th International Conference on Database and Expert Systems Applications
Clustering the tagged resources using STAC
WISM'10 Proceedings of the 2010 international conference on Web information systems and mining
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In recent years, folksonomy becomes a hot topic in many research fields such as complex systems, information retrieval, and recommending systems. It is essential to study the semantic relationships among tags in folksonomy applications. The main contributions of this paper includes: (a) proposes a general framework for the analysis of the semantic relationships among tags based on their co-occurrence. (b)investigates eight correlation measurements from various fields; then appliying these measurements to searching similar tags for a given tag on datasets from del.icio.us. (c) conducts a comparative study on both accuracy and time performance of the eight measurements. From the comparison, a best overall correlation measurement is concluded for similar tags searching in the applications of folksonomy.