基於項目的協同過濾推薦系統應用於短期項目之設計與評估

Designing and Evaluating Item-based Collaborative Filtering Recommendation Schemes for Short-period Items

黃龍華(Wijaya, Aditya Utama)

指導教授 ： 雷松亞

國立清華大學/科技管理學院/國際專業管理碩士班(IMBA)/碩士(2017年)

摘要

N/A

關鍵字

推薦系統 ； 以項目為基礎之協同過濾 ； 貼圖 ； 相似度測量 ； 個人化推薦 ； 非個人化推薦 ； 時間區間 ； 喜好

並列摘要

In the last 20 years, recommendation system has been becoming more and more widely used in many web and mobile applications. It was started when Amazon popularized a recommendation technique called item-based collaborative filtering. This technique is fast, stable, and it performs well in most media sharing contexts. However, we found that there are some serious differences in recommending “stickers”, compared to traditional media items, like movies, songs, and so on. We have tried several approaches to improve recommendations in this context by comparing different similarity measurement methods, comparing personalized and non-personalized recommendations, and altering the time range used for generating the recommendation lists. We found that, in the situation where preference measurement does not have upper-bound, adjusted cosine similarity and cosine similarity methods perform better than Pearson correlation method. Meanwhile, in the situation where items have short-lived popularity period, straightforward personalized recommendations give bad accuracy. Finally, the personalized recommendations show performance improvement when generated using shorter time range.

並列關鍵字

recommendation system ； item-based collaborative filtering ； stickers ； similarity measurement ； personalized recommendations ； non-personalized recommendations ； time range ； preference

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