A bit-string longest-common-subsequence algorithm
Information Processing Letters
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
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
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
Semantic text similarity using corpus-based word similarity and string similarity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A method for finding groups of related herbs in traditional chinese medicine
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
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Learning traditional Chinese medicine knowledge from the digital library is becoming more and more important these days in China. In medicine learning, many readers want to find out the intrinsic relation between two medicines or among thousands of medicines. A semantic recommender system is useful for readers to understand something quickly by means of analogy which is a cognitive process of transferring information from a particular subject to another if they are similar in some aspects. In view of these above, we present a novel recommender framework called Msuggest to give the diverse semantic recommended medicine terminologies and book pages when a reader searching for medicine information in digital library. Users can choose various aspects including medicine property, efficacy, clinical application, place of origin, book provenance and etc. to see different recommended results. We evaluate Msuggest under the t-test on the samples from random sampling. The result shows that Msuggest is effective and efficient in giving the recommended words and book pages.