IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Modern Information Retrieval
Efficient top-k querying over social-tagging networks
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A social inverted index for social-tagging-based information retrieval
Journal of Information Science
Discerning actuality in backstage: comprehensible contextual aging
EC-TEL'12 Proceedings of the 7th European conference on Technology Enhanced Learning
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In social tagging sites, users are provided easy ways to create social networks, to post and share items like bookmarks, videos, photos and articles, along with comments and tags. In this paper, we present a study of top-k search in social tagging sites by utilizing multiple social networks and temporal information. In particular, besides the global connection, we consider two main social networks, namely the friendship and the common interest networks in our scoring functions. Based on the degree of participation in various networks, users can be categorized into specific classes that differ in their weights on each scoring component. Temporal information, usually ignored by previous works, can enhance the popularity and freshness of the ranking results. Experiments and evaluations on real social tagging datasets show that our framework works well in practice and give useful and intuitive results.