GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Getting to know you: learning new user preferences in recommender systems
Proceedings of the 7th international conference on Intelligent user interfaces
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Taxonomy-driven computation of product recommendations
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Fast Random Walk with Restart and Its Applications
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
IEEE Transactions on Knowledge and Data Engineering
A random walk method for alleviating the sparsity problem in collaborative filtering
Proceedings of the 2008 ACM conference on Recommender systems
Detecting innovative topics based on user-interest ontology
Web Semantics: Science, Services and Agents on the World Wide Web
Transfer learning for collaborative filtering via a rating-matrix generative model
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
TANGENT: a novel, 'Surprise me', recommendation algorithm
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Inferring user's preferences using ontologies
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
OSS: a semantic similarity function based on hierarchical ontologies
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
ItemRank: a random-walk based scoring algorithm for recommender engines
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Recommendations Over Domain Specific User Graphs
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Serendipitous recommendation for scholarly papers considering relations among researchers
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Taxonomy-Oriented recommendation towards recommendation with stage
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
User similarity from linked taxonomies: subjective assessments of items
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Product recommendation with temporal dynamics
Expert Systems with Applications: An International Journal
Collaborative filtering by analyzing dynamic user interests modeled by taxonomy
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
Hi-index | 0.00 |
Most recommender algorithms produce types similar to those the active user has accessed before. This is because they measure user similarity only from the co-rating behaviors against items and compute recommendations by analyzing the items possessed by the users most similar to the active user. In this paper, we define item novelty as the smallest distance from the class the user accessed before to the class that includes target items over the taxonomy. Then, we try to accurately recommend highly novel items to the user. First, our method measures user similarity by employing items rated by users and a taxonomy of items. It can accurately identify many items that may suit the user. Second, it creates a graph whose nodes are users; weighted edges are set between users according to their similarity. It analyzes the user graph and extracts users that are related on the graph though the similarity between the active user and each of those users is not high. The users so extracted are likely to have highly novel items for the active user. An evaluation conducted on several datasets finds that our method accurately identifies items with higher novelty than previous methods.