Hae-sung Lee, Soon-young Kim, Jay-hoon Kim, Jeong-hwan Kim, Journal of Internet Computing and Services, Vol. 17, No. 5, pp. 75-88, Oct. 2016
10.7472/jksii.2016.17.5.75, Full Text:
Keywords: Recommender System, Recommendation, citation networks, personalized academic contents, log analysis, usage statics

Abstract

With the exploration of digital academic information, it is certainly required to develop more effective academic contents recommender system in order to accommodate increasing needs for accessing more personalized academic contents. Considering historical usage data, the academic content recommender system recommends personalized academic contents which corresponds with each user's preference. So, the academic content recommender system effectively increases not only the accessibility but also usability of digital academic contents. In this paper, we propose the new journal recommendation technique based on information of journal subscription and web usage logs in order to properly recommend more personalized academic contents. Our proposed recommendation method predicts user's preference with the institution similarity, the journal similarity and journal importance based on citation relationship data of references and finally compose institute-oriented recommendations. Also, we develop a recommender system prototype. Our developed recommender system efficiently collects usage logs from distributed web sites and processes collected data which are proper to be used in proposed recommender technique. We conduct compare performance analysis between existing recommender techniques. Through the performance analysis, we know that our proposed technique is superior to existing recommender methods.

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