Research On Diversified Online Ktv Music Recommendation Algorithms

While the Internet is ever growing rapidly, online multimedia entertainment is becoming more and more popular. As a new form of online multimedia entertainment, online KTV also attracts more attention. But due to the effects of the so-called information overload, people find it hard to pick proper songs from thousands of candidates. Some who know little, may take a long time to find a familiar song. While others who know much, may be overwhelmed by all the available content. Therefore, it is important to have a recommender system, helping people pick up their preferred songs.However, scores in a online KTV system represent users’ prformances rather than their preferences and singing records seems to have sequential property. Thus, recommender systems such as collaborative filtering cannot be directly applied here. This thesis built models for both users and songs, and made an attempt for online KTV music recommendation. The major work and contributions are as follows:1. Learnt useful information from user singing history, built relationships among songs and users. Proposed the Personalized Markov Embedding model and made recommendations based on users’ long-term and short preferences.2. Proposed a new goal function for recommendation, considering the diversity of songs’distribution in model space. Made this consideration adjustable in order not to affect the accuracy of the recommendation too much.3. Experimental results on a real world dataset showed that models and algorithms proposed in our thesis can make better recommendations for individuals, and the diversified model also worked as expected.