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http://dx.doi.org/10.3745/KTSDE.2015.4.6.253

Weighted Window Assisted User History Based Recommendation System  

Hwang, Sungmin (전남대학교 전자컴퓨터공학부)
Sokasane, Rajashree (전남대학교 전자컴퓨터공학부)
Tri, Hiep Tuan Nguyen (전남대학교 전자컴퓨터공학부)
Kim, Kyungbaek (전남대학교 전자컴퓨터공학부)
Publication Information
KIPS Transactions on Software and Data Engineering / v.4, no.6, 2015 , pp. 253-260 More about this Journal
Abstract
When we buy items in online stores, it is common to face recommended items that meet our interest. These recommendation system help users not only to find out related items, but also find new things that may interest users. Recommendation system has been widely studied and various models has been suggested such as, collaborative filtering and content-based filtering. Though collaborative filtering shows good performance for predicting users preference, there are some conditions where collaborative filtering cannot be applied. Sparsity in user data causes problems in comparing users. Systems which are newly starting or companies having small number of users are also hard to apply collaborative filtering. Content-based filtering should be used to support this conditions, but content-based filtering has some drawbacks and weakness which are tendency of recommending similar items, and keeping history of a user makes recommendation simple and not able to follow up users preference changes. To overcome this drawbacks and limitations, we suggest weighted window assisted user history based recommendation system, which captures user's purchase patterns and applies them to window weight adjustment. The system is capable of following current preference of a user, removing useless recommendation and suggesting items which cannot be simply found by users. To examine the performance under user and data sparsity environment, we applied data from start-up trading company. Through the experiments, we evaluate the operation of the proposed recommendation system.
Keywords
Recommendation System; Content-Based Filtering; User History; Window;
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