近年來電影工業發展蓬勃，每年都有上百部的電影推陳出新，而一般人一年看的電影可能才十至二十部。使用者不僅無法快速吸收這麼大量的資訊，想從中挑選出中意的電影更是難上加難。因此，電影推薦作為研究主題越來越受到矚目。在電影推薦系統中，處理新電影是以內容導向的電影推薦系統為主流，因其不使用使用者資訊，只用電影內容的資訊作為輸入的特性，使其不管面對新舊電影都可以保持一樣的表現。
本論文提出了純內容導向電影推薦系統，將電影的相關資訊如導演、演員、類型…等等做為輸入來推薦電影。本系統利用類神經網路對電影的內容資訊進行特徵抽取，用抽取出的向量計算電影間的相似度，並以此為基準進行推薦。我們使用Movielens-20M資料集來測試我們的系統，實驗結果證明了使用者在選擇電影時，確實會考量電影的相關資訊。

In recent year, movie industry is getting prosper. There are hundreds of movie released every year. However, it is difficult to notice the releasing of every movie, not to mention actually seeing it. Therefore, movie recommender system has become more and more popular as a research topic. Among a variety of movie recommender systems, content-based methods always ring a bell when it comes to recommending new movie. Content-based method use content of movie as input so that it does not suffer from “cold-start” problem.
In this paper, we propose the Fully Content-based Movie Recommender System (FCMR) to recommend movies to users. The proposed method trains a neural network model, Word2Vec CBOW, with content information (e.g., cast, crew, etc.) as the training data to obtain vector form features of each elements, and then take advantage of the linear relationship of learned feature to calculate the similarity between each movie. In the end, the proposed FCMR recommends movies based on the similarity. The experiments are conducted on a massive real world dataset, and the intuition behind our proposed method has been proven by the experiment results.

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