串流資料演算法於推薦系統的應用

Application of Streaming Data Algorithms to Recommender System

廖瑋婕(Wei-Chieh Liao)

指導教授 ： 陳景祥

淡江大學/商管學院/統計學系應用統計學碩士班/碩士(2018年)

https://doi.org/10.6846/TKU.2018.00852

摘要

近年來，由於資訊科技的發達，資料的種類越來越多元，因此串流資料（Data stream）也成為重要的研究領域，例如電子商務採購、網頁點擊資料、電話通訊資料、信用卡交易資訊等等。這些資料需依個別記錄按照順序處理，這樣才能快速因應所發生的各種狀況，並在需要時即時做出反應。
本論文探討分群串流資料演算法（clustering data stream algorithm）應用在推薦系統時，是否能更有效率的給予即時且精準的推薦商品，並以公開的MovieLen資料集中使用者對於電影的評分資訊，比較K-means與Affinity Propagation Clustering這兩種分群算法，比較推薦結果的差異，以達到最佳推薦，發掘用戶潛在喜好的商品為目標。

關鍵字

推薦系統 ； 串流資料 ； K-means分群 ； Affinity Propagation分群

並列摘要

In recent years, due to the development of information technology and the variety of data types, streaming data has become an important research area, such as e-commerce purchasing, web page click data, telephone communication data, credit card transaction information, etc. Since the large volumes of data arriving in a stream, most traditional algorithms might be not efficient.
This thesis discusses whether the data stream clustering algorithm can be used to recommend real-time and accurate recommended products. Based on MovieLens movie review dataset, we develop and compare movie recommender system using data stream clustering algorithm to achieve the best recommendation and explore the potential products of users.

並列關鍵字

K-means clustering ； Affinity Propagation clustering ； Recommender System ； data stream

參考文獻

英文文獻

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3. Bu J., Shen X., Xu B., Chen C., He X., Cai D.(2016). Improving Collaborative Recommendation via User-Item Subgroups, IEEE Transactions on Knowledge and Data Engineering,28,9,pp.2363-2375

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