混合式項目推薦排序演算法於健康知識網站之應用

From International Telecommunication Union (ITU) Internet population 2006 to 2009 observation report, Internet usage is increasing in countries of the world. The richness and diversity of online information resource has made the Internet to flourish. Researchers observed that people searching online health information has shown an exponential growth trend. The impact has influenced the health industry and becoming one of the considerable methods of obtaining health information.

The information overloading has decreased the efficacy and efficiency of online retrieval. To overcome these problems, search engine and hierarchical structured portal system has been emerged. In the search engine (like: Google), users could retrieve related information through keywords search. As to the directory service portal system (like: health99 or Yahoo), users will browse top to down inside the systems’ directories to obtain and learn information which interest them. Health information could bring benefits such as knowledge increment, self-care management capacity increment, clinical cost reduction, patient’s medical services satisfaction increment and doctor-patients relation strengthening. However, users may encounter difficulties while looking for health information on the Internet, such as (1) information overloading (2) disorganization (3) mass of information (related and unrelated) (4) imprecise or inappropriate search term (5) innaccessible or overly technical language (6) quality and reliability.

Users may encountered problems while searching online health information such as (1) unrefined query result (related and unrelated) (2) imprecise or inappropriate keywords decreasing search efficiency (3) lack of words feature (media resources) on the online documents may decreased probability being searched or augment ranking position from retrieving results. On the other hand, user could also find information through system like yahoo portal system, which organized information in a hierarchical structure. Sitemape can provide navigation information across website sections. However, (1) the internet is uncontrolled and unwatched, without notification, information may disappear, functions may changed or contents could be moven one place another from time to time (2) cognitive differences between users and website designers (3) Increase numerber of the hierarchical layers may also decrease the probability of items of being searched.

In our study, 12.53% of user have used search engine, among them 7.86% have used site search, which shows most of users do not use search tool. The conveniency of search engine has provided convenient and rapid way to retrieve information. However, users found their interest by confirming each retrieved result, this leads a lower precision rate compare to hierarchical structure portal system. On the other side, less retrieving result due to collection and classification difficulties, portal system has lower recall rate compare to the search engine. In order to efficiently and activately provide information, researchers started research on Information Filtering, also known for its recommendation system.

System analyzed and recommends information related to user’s interest from their visiting pattern. Furthermore, there might exist meaningless interest inside the visiting pattern and affect the recommendation result, therefore we need to remove the noise before analyzing related interest. We express the relation of related items by using correlation coefficient. However, item’s accessibility might affect the correlation coefficient, besides it didn’t concerned the relevance feedback magnitude. In our research, we have merged a new sorting method concerning the correlation coefficient, relevance feedback magnitude and item accessibility.

We assessed SORS with Health 99 Internet Information Service Log, operated by the Bureau of Health Promotion, Taiwan. The visiting patterns were divided by users into 80% training data and 20% testing data. The recommendation list is obtained by calculating related item to a certain item (Target) from the training data. The comparison between SORS and KNN (K Nearest Neighbor) was made. We discovered a better result performance while Top N is equal or smaller in SORS. Under different experiment parameter (Top N: size of recommendation list, LEN: time length used to recommendation analysis) we compare recall, precision and system performance between SORS and KNN, and discovered a better recall and precision of SORS under smaller or equal condition compare to KNN.