Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Combining link and content analysis to estimate semantic similarity
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Algorithmic detection of semantic similarity
WWW '05 Proceedings of the 14th international conference on World Wide Web
Scaling link-based similarity search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Combining content and link for classification using matrix factorization
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Tag-based social interest discovery
Proceedings of the 17th international conference on World Wide Web
An effective semantic search technique using ontology
Proceedings of the 18th international conference on World wide web
Combining anchor text categorization and graph analysis for paid link detection
Proceedings of the 18th international conference on World wide web
LSA as ground truth for recommending "flickr-aware" representative tags
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications
Tag recommendation by machine learning with textual and social features
Journal of Intelligent Information Systems
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Finding similar users is one of the probable applications in social media. The similarity between users can be measured in two different approaches: the semantic similarity and the similarity in terms of social relations. These two approaches can be combined with different weight factors. However, the conventional combination scheme has a critical drawback that the weight factors are fixed for every user and thus it is not optimized at those users that are using rare terms or do not have sufficient relations with other users. To address this problem, in this paper, we propose an adaptive combination scheme of tag-based similarity and link-based similarity in which the weight factors are dynamically determined for each user by evaluating each user's characteristics such as tag commonness and link strength. The experimental results with a Flickr data set show that the proposed scheme consistently outperforms the previous work by about 20%.