GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
The stochastic approach for link-structure analysis (SALSA) and the TKC effect
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
On the recommending of citations for research papers
CSCW '02 Proceedings of the 2002 ACM conference on Computer supported cooperative work
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Learning to Create Customized Authority Lists
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Algorithms for estimating relative importance in networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Enhancing digital libraries with TechLens+
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Making recommendations better: an analytic model for human-recommender interaction
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Node similarity in the citation graph
Knowledge and Information Systems
Recommending scientific articles using citeulike
Proceedings of the 2008 ACM conference on Recommender systems
FlexRecs: expressing and combining flexible recommendations
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
OSUSUME: cross-lingual recommender system for research papers
Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation
User-centered profile representation for recommendations across multiple content domains
International Journal of Knowledge-based and Intelligent Engineering Systems
Finding relevant papers based on citation relations
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Adding structure to top-k: from items to expansions
Proceedings of the 20th ACM international conference on Information and knowledge management
Collaborative Filtering Recommender Systems
Foundations and Trends in Human-Computer Interaction
Recommender systems: from algorithms to user experience
User Modeling and User-Adapted Interaction
When recommenders fail: predicting recommender failure for algorithm selection and combination
Proceedings of the sixth ACM conference on Recommender systems
Modeling collaborative knowledge of publishing activities for research recommendation
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
Research paper recommender system evaluation: a quantitative literature survey
Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation
See what you want to see: visual user-driven approach for hybrid recommendation
Proceedings of the 19th international conference on Intelligent User Interfaces
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All new researchers face the daunting task of familiarizing themselves with the existing body of research literature in their respective fields. Recommender algorithms could aid in preparing these lists, but most current algorithms do not understand how to rate the importance of a paper within the literature, which might limit their effectiveness in this domain. We explore several methods for augmenting existing collaborative and content-based filtering algorithms with measures of the influence of a paper within the web of citations. We measure influence using well-known algorithms, such as HITS and PageRank, for measuring a node's importance in a graph. Among these augmentation methods is a novel method for using importance scores to influence collaborative filtering. We present a task-centered evaluation, including both an offline analysis and a user study, of the performance of the algorithms. Results from these studies indicate that collaborative filtering outperforms content-based approaches for generating introductory reading lists.