The Application of Social Tagging Based Collaborative Filtering Personal Recommender Strategy in Electricity Market

In the Internet world, when people access to the information, they are also providing information to others. Therefore, how to find valuable information from the vast amounts of information in order to meet the user's needs, and how to find and enjoy the valuable information by the required users, have been a hot issue which is concerned by academia and the business. Collaborative filtering (CF) and social tagging are the most widely recommendation techniques. In this paper, tag-based collaborative filtering algorithm is proposed to the electricity market. The individual requirement can be satisfied according to different power consumers. This new algorithm can mine the potential preferences of users, and then recommend items in the user's preferences scope. This method can improve the traditional collaborative filtering methods, and can solve the single interest model problem of traditional methods. The experiments based on electricity consumer data set shows that the tag-based collaborative filtering method is significantly better than the traditional collaborative filtering methods in recommendation effects.