GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Information foraging in information access environments
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Personalization on the Net using Web mining: introduction
Communications of the ACM
Web usage mining for Web site evaluation
Communications of the ACM
Automatic personalization based on Web usage mining
Communications of the ACM
Does “authority” mean quality? predicting expert quality ratings of Web documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 11th international conference on World Wide Web
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Probabilistic User Behavior Models
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
SERF: integrating human recommendations with search
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Collaborative Filtering with Maximum Entropy
IEEE Intelligent Systems
IEEE Transactions on Knowledge and Data Engineering
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Beyond PageRank: machine learning for static ranking
Proceedings of the 15th international conference on World Wide Web
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
A large-scale evaluation and analysis of personalized search strategies
Proceedings of the 16th international conference on World Wide Web
AdaRank: a boosting algorithm for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Popularity weighted ranking for academic digital libraries
ECIR'07 Proceedings of the 29th European conference on IR research
Low-complexity fuzzy relational clustering algorithms for Web mining
IEEE Transactions on Fuzzy Systems
Designing business-intelligence tools with value-driven recommendations
DESRIST'10 Proceedings of the 5th international conference on Global Perspectives on Design Science Research
Personalized search in digital libraries via spreading activation model
Web Intelligence and Agent Systems
Hi-index | 0.00 |
Given the exponential increase of indexable context on the Web, ranking is an increasingly difficult problem in information retrieval systems. Recent research shows that implicit feedback regarding user preferences can be extracted from web access logs in order to increase ranking performance. We analyze the implicit user feedback from access logs in the CiteSeer academic search engine and show how site structure can better inform the analysis of clickthrough feedback providing accurate personalized ranking services tailored to individual information retrieval systems. Experiment and analysis shows that our proposed method is more accurate on predicting user preferences than any non-personalized ranking methods when user preferences are stable over time. We compare our method with several non-personalized ranking methods including ranking SVMlight as well as several ranking functions specific to the academic document domain. The results show that our ranking algorithm can reach 63.59% accuracy in comparison to 50.02% for ranking SVMlight and below 43% for all other single feature ranking methods. We also show how the derived personalized ranking vectors can be employed for other ranking-related purposes such as recommendation systems.