臉書粉絲專頁之匯流與推薦系統

Facebook (FB, 臉書)是現今臺灣最常使用的社群網站，是我們生活中不可或缺的一部分。話雖如此，令人愛戴的FB還是造成了許多隱私上疑慮，我們常在無意間就透漏了隱私且敏感的資訊。不僅如此，FB也主導了我們的閱聽權，在面對宛如洪水般爆炸的臉書資訊時，我們也只能被動地接受由臉書演算法計算推薦的文章。因此本論文中所提出的 FB Interests Recommendation System (FBIRS) 在能夠保護使用者隱私的情況需要透過系統便能取得感興趣的內容。最後，藉由使用者對推薦結果的評分，蒐集使用回饋，持續改進推薦系統的方法與準確性。整個FBIRS以Docker container技術實作，易於輕鬆部屬到任何平台或雲端系統。

關鍵字

推薦系統；資料探勘；臉書；粉絲專頁；喜好模型

並列摘要

Facebook (FB) is currently the most popular social website in the world so that more than 1.5 billion people are resident of FB and use FB services in their daily life. However, some public social activities cause privacy controversies due to the leak of sensitive information. Furthermore, FB was criticized for controlling the power of recommending contents through the largest social platform. Thinking from Facebook’s perspective, its recommendation method must be trained for several purposes, such as Ads, events, and social opinions, rather than for personalized services. Therefore, we design and develop the FB Interests Recommendation System (FBIRS) to make FB recommendations be fully controlled personally. By analyzing user logs to incrementally build the user-favorite model, FBIRS gradually recommends collected contents that are interested by the user. First, FB crawler is implemented to gather every post from selected FB Pages. Then, analyzing posts through phrase extraction and keyword weighting so that feature selection can estimate the most important keywords as features of the topic model. Similar favorite model of the user is built from logs containing user behaviors of reading, collecting and “like/dislike” posts. FBIRS performs matching process on both topic and favorite models to recommend fresh content posts to the user and get feedback data again to adjust both models. Consequently, FBIRS gradually improves the performance of recommendations. The whole FBIRS is implemented with Docker container technology so that it can be ported to any OS or cloud platform easily and efficiently.