隨著網際網路日益進步，越來越多的電子商務網站都使用著推薦系統來幫助消費者找到要購買的商品。一般傳統常見的推薦系統都是考量瀏覽商品與商品之間的關聯性，或是基於內容過濾和協同過濾的方式為消費者進行推薦，但這些推薦系統都沒有考量到消費者在網路上進行商品瀏覽時，可能因為習慣或喜好而產生出不同的商品瀏覽序列行為。
本研究分析了個案電商平台當中五大類商品的瀏覽序列資料，考量須以序列方式進行探勘，因此採用重疊分群結合長短期記憶神經網路(Long Short Term Memory Network, LSTM) 的方法於電商平台當中對用戶於商品瀏覽序列做預測。
本研究以消費者角度出發，隨著消費者在平台中瀏覽商品的序列越來越長時，能有越來越精準的推薦結果，除了能夠為消費者提供合適、有價值性的資訊以外，對公司而言，也能夠使消費者產生依賴性而建立自身長期經營的市場競爭力。經由本研究的結果也顯示，在瀏覽序列紀錄檔中雖然多數序列都存在無嚴格序列的問題，但使用重疊分群能有效解決此問題，且在預測的準確率上也與不採用重疊分群的效果高出25%。

Nowadays, an increasing number of E-commerce websites are utilizing recommendation systems to suggest users find products to purchase, and enrich their shopping potential. Generally, one parts of the traditional recommendation system considered the correlation between products, others are use Content Base Filtering or Collaborative Filtering, but the sequence of the actions performed by them are not usually be considered. Different browsing sequence behaviors may occur due to user’s habits or preferences.
To address this issue, this research analyzes the recommendation systems at five categories of products by considered the recommendation systems in a sequential pattern. We use a hybrid approach, which employing overlapping clustering and Long Short-Term Memory Network (LSTM) to make next browsing sequential products prediction.
From the consumer perspective, while consumers browsing products longer and longer, they will get better accurate recommendation results. When the consumers are satisfied with a particular E-commerce sites, they will purchase there more. It can also keep company staying competitive in the market. The results of this research shows that although most sequences in the log file faced the non-strict sequence problem, we use overlapping clustering can solve this problem effectively. To compare with non-using overlapping clustering model, use overlapping clustering’s model prediction accuracy is 25% higher than non-using overlapping clustering.

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