以人機合作的方式來評斷大量數位作品

Abstract

本研究提出了一個以人和代理人合作來評斷大量主觀作品的模型。主要概念是以演化式計算的方法來訓練一群能夠察覺使用者喜好的代理人，在架構中代理人扮演著中介的角色，以這些訓練過的代理人來逐一評斷物件，省去由使用者親自評斷的大量時間，根據評分的結果進而推薦使用者可能有興趣的作品。此模型可以應用在許多類型的作品評斷之上，像是音樂，圖畫，電影…等。本研究中以此概念實作一個個人化音樂推薦系統。在此系統中使用了八種音樂特徵來表示樂曲的特性，並且藉由互動式演化計算的方式，讓使用者直接訓練一群能符合該使用者喜好的代理人，此外也提出一些機制來減少因人類參與演化所會帶來的問題。最後進行一系列的實驗來證實以此模型實作的推薦系統的確有良好的效能，並提出此模型的其他應用方法。

This thesis presents a model which evaluates a large number of subjective compositions by the cooperation between human and agents. The main concept is to train a group of agents which satisfy the user’s taste by evolutionary computing. The agents play the role of an intermediary in the model and evaluate all compositions for the user. For this reason, the user can reduce the time of directly evaluating. According to the results of the agents’ evaluations, the system could further recommend the items the user may be interested in. This model could consider many kinds of compositions, like music, pictures, movies, and so on. We implement a personalized music recommendation system with this framework. The system applies eight kinds of musical features to represent the music items, and let the user directly train a group of agents which fit the user’s preference by interactive evolutionary computing. Furthermore, we present some additional mechanisms to reduce the problems which result from the human beings participating in the evolution. Finally, a series of experiments are executed to show that our approach performs well.