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
Proceedings of the 11th international conference on World Wide Web
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
Collaborative filtering with decoupled models for preferences and ratings
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Effective missing data prediction for collaborative filtering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
EigenRank: a ranking-oriented approach to collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A new rank correlation coefficient for information retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Journal of Artificial Intelligence Research
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Scholarly paper recommendation via user's recent research interests
Proceedings of the 10th annual joint conference on Digital libraries
A social network-aware top-N recommender system using GPU
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Serendipitous recommendation for scholarly papers considering relations among researchers
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Personalized book recommendations created by using social media data
WISS'10 Proceedings of the 2010 international conference on Web information systems engineering
IPKB: a digital library for invertebrate paleontology
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
Exploiting potential citation papers in scholarly paper recommendation
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
What to read next?: making personalized book recommendations for K-12 users
Proceedings of the 7th ACM conference on Recommender systems
Research paper recommender system evaluation: a quantitative literature survey
Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation
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A recommender system is useful for a digital library to suggest the books that are likely preferred by a user. Most recommender systems using collaborative filtering approaches leverage the explicit user ratings to make personalized recommendations. However, many users are reluctant to provide explicit ratings, so ratings-oriented recommender systems do not work well. In this paper, we present a recommender system for CADAL digital library, namely CARES, which makes recommendations using a ranking-oriented collaborative filtering approach based on users' access logs, avoiding the problem of the lack of user ratings. Our approach employs mean AP correlation coefficients for computing similarities among users' implicit preference models and a random walk based algorithm for generating a book ranking personalized for the individual. Experimental results on real access logs from the CADAL web site show the effectiveness of our system and the impact of different values of parameters on the recommendation performance.