Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
IEEE Transactions on Knowledge and Data Engineering
A user-item relevance model for log-based collaborative filtering
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
Integrating tags in a semantic content-based recommender
Proceedings of the 2008 ACM conference on Recommender systems
Tag-based user modeling for social multi-device adaptive guides
User Modeling and User-Adapted Interaction
ICSOC '08 Proceedings of the 6th International Conference on Service-Oriented Computing
Adaptive systems in the era of the semantic and social web, a survey
User Modeling and User-Adapted Interaction
Challenges in Personalizing and Decentralizing the Web: An Overview of GOSSPLE
SSS '09 Proceedings of the 11th International Symposium on Stabilization, Safety, and Security of Distributed Systems
User Modeling and User-Adapted Interaction
Extending a hybrid tag-based recommender system with personalization
Proceedings of the 2010 ACM Symposium on Applied Computing
Improving the accuracy of tagging recommender system by using classification
ICACT'10 Proceedings of the 12th international conference on Advanced communication technology
Collaborative filtering in social tagging systems based on joint item-tag recommendations
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Mining social tags to predict mashup patterns
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
A recommender system based on tag and time information for social tagging systems
Expert Systems with Applications: An International Journal
Inferring word relevance from eye-movements of readers
Proceedings of the 16th international conference on Intelligent user interfaces
Latent subject-centered modeling of collaborative tagging: An application in social search
ACM Transactions on Management Information Systems (TMIS)
Overview and analysis of personal and social tagging context to construct user models
Proceedings of the 2nd Workshop on Context-awareness in Retrieval and Recommendation
A user profile modelling using social annotations: a survey
Proceedings of the 21st international conference companion on World Wide Web
Using inferred tag ratings to improve user-based collaborative filtering
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Finding related micro-blogs based on wordnet
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications
User-based collaborative filtering on cross domain by tag transfer learning
Proceedings of the 1st International Workshop on Cross Domain Knowledge Discovery in Web and Social Network Mining
A folksonomy-based recommender system for personalized access to digital artworks
Journal on Computing and Cultural Heritage (JOCCH)
A novel user-based collaborative filtering method by inferring tag ratings
ACM SIGAPP Applied Computing Review
A Random Walk Model for Item Recommendation in Social Tagging Systems
ACM Transactions on Management Information Systems (TMIS)
A framework for tag-aware recommender systems
Expert Systems with Applications: An International Journal
CooL-AgentSpeak: Endowing AgentSpeak-DL agents with plan exchange and ontology services
Web Intelligence and Agent Systems
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Considering the natural tendency of people to follow direct or indirect cues of other people's activities, collaborative filtering-based recommender systems often predict the utility of an item for a particular user according to previous ratings by other similar users. Consequently, effective searching for the most related neighbors is critical for the success of the recommendations. In recent years, collaborative tagging systems with social bookmarking as their key component from the suite of Web 2.0 technologies allow users to freely bookmark and assign semantic descriptions to various shared resources on the web. While the list of favorite web pages indicates the interests or taste of each user, the assigned tags can further provide useful hints about what a user thinks of the pages. In this paper, we propose a new collaborative filtering approach TBCF (Tag-based Collaborative Filtering) based on the semantic distance among tags assigned by different users to improve the effectiveness of neighbor selection. That is, two users could be considered similar not only if they rated the items similarly, but also if they have similar cognitions over these items. We tested TBCF on real-life datasets, and the experimental results show that our approach has significant improvement against the traditional cosine-based recommendation method while leveraging user input not explicitly targeting the recommendation system.