推薦系統是電子商務領域中一個活躍且成熟的應用，它用於預測用戶可能感興趣之商品，並向用戶推薦該商品以增加銷售。在實踐中，推薦商品通常僅以清單方式顯示在用戶頁面中，較不具備額外行銷手段。而本文嘗試提出一種基於文字生成系統並結合推薦商品之直接行銷方法，此文字生成系統使用亞馬遜電商平台上正面及幫助程度大之商品評論資料訓練Gated Recurrent Unit模型，模型學習訓練語料庫中之規律以自動生成文字。生成之文字用作直接行銷並與推薦商品共同呈現在用戶頁面或透過電子郵件及其他渠道推送。

Recommender system is an active and well-developed application in e-commerce industry. It serves to predict what a user might also be interested in other certain items and make recommendations of those items to the user for sales increase. In practice, the recommendations are usually made to the user in a list of item with direct link to the particular item. This paper propose an additional approach with natural language generation model generating text for direct marketing along with the recommended item. The text generation model applies Gated Recurrent Unit to automatically generate text based on sentimentally positive user reviews with the helpfulness above a set value from Amazon Review Datasets. The model learns from reviews which users find useful and outputs textual content regarding the quality features of the recommended product for advertising via e-mail and some other channels.

Abstract.................................1
摘要.....................................2
1. Introduction..........................4
2. Literature Review.....................5
2.1 Deep Learning........................5
2.2 Natural Language Generation..........6
3. Methodology...........................7
3.1 Datasets.............................7
3.2 Modeling Approach....................8
4. Result................................12
5. Discussion............................14
References...............................16

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