Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Bipartite graph partitioning and data clustering
Proceedings of the tenth international conference on Information and knowledge management
Machine Learning
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
ACOS'07 Proceedings of the 6th Conference on WSEAS International Conference on Applied Computer Science - Volume 6
Tag-aware recommender systems by fusion of collaborative filtering algorithms
Proceedings of the 2008 ACM symposium on Applied computing
Real-time automatic tag recommendation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Information Retrieval
Introduction to Information Retrieval
Collaborative Filtering Recommender Systems Using Tag Information
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Two-way Poisson mixture models for simultaneous document classification and word clustering
Computational Statistics & Data Analysis
Automatic tag recommendation algorithms for social recommender systems
ACM Transactions on the Web (TWEB)
| Hi-index | 0.00 |
Tagging has become increasingly popular with the explosion of user-created content on the web. A ‘tag' can be defined as a group of keywords that makes organizing, browsing and searching for content more efficient. Users apply tags to a variety of web-based, shareable content including photos, videos, news articles, bookmarks, friends, etc. Tag suggestions for blog posts or web-pages have changed the focus of the tagging process from generation to recognition, thus making it less time and effort intensive. We propose tag recommendation algorithms for personalized agents, that recommend tags for bookmarks stored in a popular social bookmarking website, Del.ici.ous [6]. Our tag recommender agents learn to classify the tags according to their semantic similarity based on collaborative tagging by the users. Hence this approach can be used to facilitate folksonomy formation for the social network. In this paper, we first empirically verify our hypothesis that web pages with similar content are tagged with similar tags. We compare both Content-based and Collaborative approaches to recommend tags to the users. We analyze the performance of two probabilistic approaches to recommend tags from users with similar tagging behavior.