Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Fab: content-based, collaborative recommendation
Communications of the ACM
A study on video browsing strategies
A study on video browsing strategies
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
The Journal of Machine Learning Research
IEEE Transactions on Knowledge and Data Engineering
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Concept-Based Document Recommendations for CiteSeer Authors
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Technical paper recommendation: a study in combining multiple information sources
Journal of Artificial Intelligence Research
Conceptual recommender system for CiteSeerX
Proceedings of the third ACM conference on Recommender systems
Research paper recommender systems: a subspace clustering approach
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
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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The past decades have witnessed the rapid development of academic research, which results in a growing number of scholarly papers. As a result, paper recommender systems have been proposed to help researchers find their interested papers. Most previous studies in paper recommendations mainly concentrate on paper-paper or user-paper similarities without taking users' reading purposes into account. It is common that different users may prefer to different aspects of a paper, e.g., the focused problem/task or the proposed solution. In this paper, we propose to satisfy user-specific reading purposes by recommending the most problem-related papers or solution-related papers to users separately. For a target paper, we use the paper citation graph to generate a set of potential relevant papers. Once getting the candidate set, we calculate the problem-based similarities and solution-based similarities between candidates and the target paper through a concept based topic model, respectively. We evaluate our models on a real academic paper dataset and our experiments show that our approach outperforms a traditional similarity based model and can provide highly relevant paper recommendations according to different reading purposes for researchers.