Msuggest
Shi Shaomin, Wei Baogang, Yan Yang · 2009
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.