相異使用者的評價於推薦系統之研究;A Study of Leveraging Dissimilar User Information in a Recommendation System

[[abstract]]推薦系統主要在推薦使用者可能感興趣的資訊，如何在眾多資訊中找到最準確的推薦方法，並且有效率地給出推薦為一大課題。在推薦的過程中往往會遇到兩種問題，一是新使用者剛加入這個系統，系統要如何去推薦給新使用者資訊的冷起始問題；另一個則是使用者所評價過的物品數過少，導致系統對使用者的偏好不夠清楚，進而無法準確推薦的稀疏問題。在解決冷起始問題上，以往作法是把資料放入演算法進行訓練前，將相似使用者的評價也一併加入訓練，本研究則是探討在以往未納入考量的相異使用者，他們的評價是否也能對系統預測的準確率產生貢獻。將前處理好的資料放入多種演算法，最後以推薦系統的準確率作為驗證。本研究使用MovieLens電影評價資料庫，運用使用者已有的評價紀錄及新增的資料，藉由LIBMF演算法及Librec系統的SVD++、Slopeone演算法做推薦預測。 Recommendation systems mainly recommend a user with information which the user may be interested in. How to find the most accurate information and also an efficient way to recommend the user is a major issue. In the process of recommendation, there are often two problems existing in collaborative filtering. The lack of information of a new user or a new item may cause a problem in finding similar users or similar items. This is called cold start problem. The sparsity problem arises when not enough ratings are provided to the items to recommend. This study uses the MovieLens film evaluation database, and our method uses the user's existing rating records to make a prediction. In order to solve the cold start problem, a common practice is to put the similar users’ information into collaborative filtering. Our proposed method is to use the dissimilar users’ information to provide additional information that may contribute to the accuracy of the system. By examining the pre-processed data into a variety of algorithms, we compare the results of our approach