User credit-based collaborative filtering
Memory-based collaborative filtering is the state-of-the-art method in recommender systems and has proven to be successful in various applications. In this paper we develop novel memory-based methods that incorporate the level of a user credit instead of using similarity between users. The user credit is the degree of one's rating reliability that measures how adherently the user rates items as others do. Preliminary simulation results show that the proposed methods outperform the conventional memory-based ones. The methods are effective in a cold-starting problem.
In the overall ownership cost of enterprise system, the maintenance cost consists of a major percentage. During the lifetime of an enterprise system, process customization is the most frequent maintenance efforts. However, current processing method has limited scalability and efficiency. In this case study, we explained how a scalable and efficient customization processing method was implemented. This method used the carbon emission trading mechanism to facilitate the cost benefit analysis of customization request. It also used distributed processing principle to improve the overall processing efficiency. Feedback from a pilot implementation in a large manufacturer included.