GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
A simple rule-based part of speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
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
Tag-aware recommender systems by fusion of collaborative filtering algorithms
Proceedings of the 2008 ACM symposium on Applied computing
YAGO: A Large Ontology from Wikipedia and WordNet
Web Semantics: Science, Services and Agents on the World Wide Web
A random walk method for alleviating the sparsity problem in collaborative filtering
Proceedings of the 2008 ACM conference on Recommender systems
Integrating tags in a semantic content-based recommender
Proceedings of the 2008 ACM conference on Recommender systems
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Detecting innovative topics based on user-interest ontology
Web Semantics: Science, Services and Agents on the World Wide Web
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting semantic relations between named entities in text using contextual features
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
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
Contextual phrase-level polarity analysis using lexical affect scoring and syntactic N-grams
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Fully automatic lexicon expansion for domain-oriented sentiment analysis
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
OSS: a semantic similarity function based on hierarchical ontologies
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Combining learning and word sense disambiguation for intelligent user profiling
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Personalized Recommender Systems Integrating Social Tags and Item Taxonomy
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
TagiCoFi: tag informed collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Using a trust network to improve top-N recommendation
Proceedings of the third ACM conference on Recommender systems
DBpedia - A crystallization point for the Web of Data
Web Semantics: Science, Services and Agents on the World Wide Web
Tensor Decompositions and Applications
SIAM Review
Sentence level sentiment analysis in the presence of conjuncts using linguistic analysis
ECIR'07 Proceedings of the 29th European conference on IR research
The task-dependent effect of tags and ratings on social media access
ACM Transactions on Information Systems (TOIS)
Classical music for rock fans?: novel recommendations for expanding user interests
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Categorising social tags to improve folksonomy-based recommendations
Web Semantics: Science, Services and Agents on the World Wide Web
Taxonomy induction based on a collaboratively built knowledge repository
Artificial Intelligence
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
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Subjective assessments (SAs), such as ''elegant'' and ''gorgeous,'' are assigned to items by users, and they are common in the reviews and tags found on many online sites. Analyzing the linked information provided by an SA assigned by a user to an item can improve the recommendation accuracy. This is because this information contains the reason why the user assigned a high or low rating value to the item. However, previous studies have failed to use SAs in an effective manner to improve the recommendation accuracy because few users rate the same items with the same SAs, which leads to the sparsity problem during collaborative filtering. To overcome this problem, we propose a novel method, called Linked Taxonomies, which links a taxonomy of items to a taxonomy of SAs to capture the user@?s interests in detail. First, our method groups the SAs assigned by users to an item into subjective classes (SCs), which are defined using a taxonomy of SAs such as those in WordNet, and they reflect the SAs/SCs assigned to an item based on their classes. Thus, our method can measure the similarity of users based on the SAs/SCs assigned to items and their classes (item classes are defined using a taxonomy of items), which overcomes the sparsity problem. Furthermore, SAs that are ineffective for accurate recommendations are excluded automatically from the taxonomy of SAs using this method. This is highly beneficial for the designers of taxonomies of SAs because it helps to ensure the production of accurate recommendations. We conducted investigations using a movie ratings/tags dataset with a taxonomy of SAs extracted from WordNet and a restaurant ratings/reviews dataset with an expert-created taxonomy of SAs, which demonstrated that our method generated more accurate recommendations than previous methods.