Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A New Method for Finding Generalized Frequent Itemsets in Generalized Association Rule Mining
ISCC '02 Proceedings of the Seventh International Symposium on Computers and Communications (ISCC'02)
FP-tax: tree structure based generalized association rule mining
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Information retrieval system evaluation: effort, sensitivity, and reliability
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Using annotations in enterprise search
Proceedings of the 15th international conference on World Wide Web
AutoTag: a collaborative approach to automated tag assignment for weblog posts
Proceedings of the 15th international conference on World Wide Web
Fundamentals of Database Systems (5th Edition)
Fundamentals of Database Systems (5th Edition)
Optimizing web search using social annotations
Proceedings of the 16th international conference on World Wide Web
P-TAG: large scale automatic generation of personalized annotation tags for the web
Proceedings of the 16th international conference on World Wide Web
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Recommending Tags for Pictures Based on Text, Visual Content and User Context
ICIW '08 Proceedings of the 2008 Third International Conference on Internet and Web Applications and Services
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Tag Recommendations in Folksonomies
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Toward Bridging the Annotation-Retrieval Gap in Image Search
IEEE MultiMedia
Tag recommendations based on tensor dimensionality reduction
Proceedings of the 2008 ACM conference on Recommender systems
Personalized, interactive tag recommendation for flickr
Proceedings of the 2008 ACM conference on Recommender systems
The MIR flickr retrieval evaluation
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Personalized tag recommendation using graph-based ranking on multi-type interrelated objects
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Latent dirichlet allocation for tag recommendation
Proceedings of the third ACM conference on Recommender systems
A content-based method to enhance tag recommendation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Demand-driven tag recommendation
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Improving tag recommendation using social networks
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Efficient Tag Recommendation for Real-Life Data
ACM Transactions on Intelligent Systems and Technology (TIST)
| Hi-index | 0.00 |
Tag recommendation is focused on recommending useful tags to a user who is annotating a Web resource. A relevant research issue is the recommendation of additional tags to partially annotated resources, which may be based on either personalized or collective knowledge. However, since the annotation process is usually not driven by any controlled vocabulary, the collections of user-specific and collective annotations are often very sparse. Indeed, the discovery of the most significant associations among tags becomes a challenging task. This article presents a novel personalized tag recommendation system that discovers and exploits generalized association rules, that is, tag correlations holding at different abstraction levels, to identify additional pertinent tags to suggest. The use of generalized rules relevantly improves the effectiveness of traditional rule-based systems in coping with sparse tag collections, because: (i) correlations hidden at the level of individual tags may be anyhow figured out at higher abstraction levels and (ii) low-level tag associations discovered from collective data may be exploited to specialize high-level associations discovered in the user-specific context. The effectiveness of the proposed system has been validated against other personalized approaches on real-life and benchmark collections retrieved from the popular photo-sharing system Flickr.