|Title:||Visualized book recommender system using matrix clustering||Authors:||Kuo, June Jei
Zhang, Yu Jung
|Keywords:||Book recommender | Matrix clustering | Time decay | Topic map||Issue Date:||1-Jan-2013||Journal Volume:||51||Journal Issue:||1||Source:||Journal of Educational Media and Library Science||Abstract:||
Traditional library recommender system can not only employ users' borrowing records to recommend books with similar subjects which they have read, but also use borrowing records of users who are in the same social network to recommend books they never borrow but may be interested in. However, as users' reading interests changes from time to time, treating their borrowing records at different time periods equally seems to lead the recommendation results not to meet the users' current needs. Moreover, as the borrowing records are highly dimensional and sparse, the traditional clustering methods cannot tackle clustering issue effectively. Besides, in order to allow users to examine recommendation results in multiple aspects and offer a clear picture of items ranked by users' perceived reading interests, interactive information visualization need to be implemented. Therefore, this paper exploits time decay weight, matrix clustering using dynamic threshold and topic maps to propose a novel visualized book recommender system. According to the experimental results of users' satisfaction questionnaire, the proposed recommender system can be useful to represent the recommendation results and helpful for users to find their interested books. Furthermore, two-layered topic map is easier to understand than one-layered topic map, and it can effectively satisfy the users' needs.
|Appears in Collections:||圖書館|
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