GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
On modeling information retrieval with probabilistic inference
ACM Transactions on Information Systems (TOIS)
Fab: content-based, collaborative recommendation
Communications of the ACM
An algorithm for suffix stripping
Readings in information retrieval
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
RecTree: An Efficient Collaborative Filtering Method
DaWaK '01 Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery
WWW '03 Proceedings of the 12th international conference on World Wide Web
The Journal of Machine Learning Research
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
A hybrid approach for searching in the semantic web
Proceedings of the 13th international conference on World Wide Web
An automatic weighting scheme for collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Journal of the American Society for Information Science and Technology
Exploring social annotations for the semantic web
Proceedings of the 15th international conference on World Wide Web
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
An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval
IEEE Transactions on Knowledge and Data Engineering
Interest-based personalized search
ACM Transactions on Information Systems (TOIS)
Applying collaborative filtering techniques to movie search for better ranking and browsing
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
Objectrank: authority-based keyword search in databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Web search personalization with ontological user profiles
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A Music Recommendation System with a Dynamic K-means Clustering Algorithm
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
Exploring folksonomy for personalized search
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Social ranking: uncovering relevant content using tag-based recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
A new approach to evaluating novel recommendations
Proceedings of the 2008 ACM conference on Recommender systems
Understanding user's query intent with wikipedia
Proceedings of the 18th international conference on World wide web
Formal concept analysis in information science
Annual Review of Information Science and Technology
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
OWL-QL-a language for deductive query answering on the Semantic Web
Web Semantics: Science, Services and Agents on the World Wide Web
A recommender system with interest-drifting
WISE'07 Proceedings of the 8th international conference on Web information systems engineering
Modern Information Retrieval
Social bookmark weighting for search and recommendation
The VLDB Journal — The International Journal on Very Large Data Bases
A probabilistic approach to semantic collaborative filtering using world knowledge
Journal of Information Science
Collaborative topic modeling for recommending scientific articles
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Approximation in formal concept analysis
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
A pragmatic approach to model and exploit the semantics of product information
Journal on Data Semantics VII
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The objective of search and recommendation is to provide users with documents that are relevant to their needs. Keyword-based search and recommendation approaches suffer from sparsity and semantic ambiguity problems because they correlate users' needs with documents only via keywords. Thus, for a given query, some documents that are semantically relevant to a user's needs are not provided if they do not include specific keywords. To address this, some search approaches have used the authority of documents, which is commonly represented using hyperlinks within documents. However, if there are no hyperlinks, it is difficult to exploit the authority for ranking documents. As the links of documents are determined by their owners, the authority derived from links does not consider users' current needs. In order to resolve these problems, we propose a unified framework for semantic search and recommendation to enrich the semantics of users' needs and documents with their corresponding concepts and to use personalized authority derived from recommendation approaches. The proposed approach makes it possible to retrieve documents with a high degree of semantic relevance as well as high authority. Through extensive experiments, we show that our approach outperforms conventional search and recommendation approaches.