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題名：基於協同主題模型與注意力機制神經網路之新聞推薦
作者：廖昱善
作者(外文)：Liao, Yu-Shan
校院名稱：國立交通大學
系所名稱：資訊管理研究所
指導教授：劉敦仁
學位類別：博士
出版日期：2020
主題關鍵詞：新聞推薦；推薦系統；協同主題模型；注意力機制神經網路；資料探勘；News Recommendation；Recommender System；Collaborative Topic Model；Attention Neural Network；Data Mining
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越來越多的使用者不再由傳統媒體，而是利用網路新聞平台取得新聞資訊。各大新聞平台戮力於給使用者大量即時的訊息，期望吸引使用者的目光，但卻因為資訊過載造成新聞讀者卻步。於是如何提供使用者真正即時又有意義的新聞，產生有效的點閱率，成了平台業者的當務之急，新聞推薦系統也因此應運而生。
新聞推薦不同於其他文檔或項目推薦，剛發佈新聞文檔時，很少有使用者瀏覽紀錄可用。如果沒有足夠的瀏覽紀錄，往往讓奠基於歷史資訊而進行推薦的傳統方法無法提供有效的推薦，也不容易為新聞讀者與新聞平台帶來額外效益。
本研究將由使用者對新聞喜好主題特徵出發，歸納出使用者喜好的使用者特徵及其新聞特徵，進行新聞推薦。利用這些對新聞喜好的特徵將使用者分群，分析各個群體的新聞偏好，並考慮到新聞熱度，將時間因子納入考量，改良新聞推薦的效能。同時，利用神經網路加上注意力機制，學習使用者個人與所屬群體本身之特徵，佐以新聞本身之特徵，試圖進一步增進新聞推薦之效能。
News websites have become a significant channel for users to have their required news. However, information overloading makes it difficult to retrieve the news articles that users really need. Therefore, news recommender systems are needed to increase the users’ loyalty, satisfaction, and browsing aspiration.
News recommendation, however, is unlike any other document or item recommendation. Few user records are available in the beginning when a news document is just being published. That is, without enough browsing records, recommender systems cannot provide effective suggestions based on traditional approach, and thus bring little benefits to news readers and news websites.
In this study, taking information of individual users and the group the users belong to into consideration, we propose a news recommendation approach based on collaboration topic model via analyzing the news preference from individual user and user group perspectives. In addition, the proposed approach takes time factor into consideration, because old news are usually less interesting to news readers. Meanwhile, using the latent factors derived from collaborative topic model, neural networks with attention-based mechanism have been incorporated in this study to further improve the recommendation performance.
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