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POUPM: An Efficient Algorithm for Mining Partial Order User Preferences
Abstract
Mining user preferences plays a critical role in many important applications such as customer relationship management, product and personalized service recommendation. Although of great potential, to the best of our knowledge, the problem of mining user preferences from positive and negative examples has not been explored before. In this paper, we identify and model the problem systematically. Our theoretical problem analysis indicates that mining preferences from positive and negative examples is challenging. We develop a greedy algorithm called POUPMA and show the effectiveness and the efficiency of the algorithm using synthetic data sets.
The Dynamic Traffic Information publish platform is a core component of the ITS information service system, which focuses on the function of retrieving ITS data from background servers and delivering them to the registered client terminators. The Intelligent Transportation System data format is characterized by its diversity and the complexity, and the traditional data representation cannot fulfill the performance goal of ITS data publish. For this reason, a new publish platform of Dynamic Traffic Information was discussed in this paper. Basing on the latest DATEX II data exchange standard, the platform’s scalability and functionality were greatly enhanced.
The Dynamic Traffic Information publish platform is a core component of the ITS information service system, which focuses on the function of retrieving ITS data from background servers and delivering them to the registered client terminators. The Intelligent Transportation System data format is characterized by its diversity and the complexity, and the traditional data representation cannot fulfill the performance goal of ITS data publish. For this reason, a new publish platform of Dynamic Traffic Information was discussed in this paper. Basing on the latest DATEX II data exchange standard, the platform’s scalability and functionality were greatly enhanced.
Personalization of e-services poses new challenges to database technology, demanding a powerful and flexible modeling technique for complex preferences. Preference queries have to be answered cooperatively by treating preferences as soft constraints, attempting a best possible match-making. We propose a strict partial order semantics for preferences, which closely matches people's intuition. A variety of natural and of sophisticated preferences are covered by this model. We show how to inductively construct complex preferences by means of various preference constructors. This model is the key to a new discipline called preference engineering and to a preference algebra. Given the Best-Matches-Only (BMO) query model we investigate how complex preference queries can be decomposed into simpler ones, preparing the ground for divide & conquer algorithms. Standard SQL and XPATH can be extended seamlessly by such preferences (presented in detail in the companion paper [15]). We believe that this model is appropriate to extend database technology towards effective support of personalization.
Human Computer Interaction (HCI) challenges in mobile computing can be addressed by tailoring access and use of mobile services to user preferences. Our investigation of existent approaches to personalisation in context-aware computing found that user preferences are assumed to be static across different context descriptions, whilst in reality some user preferences are transient and vary with the change in context. Furthermore, existent preference models do not give an intuitive interpretation of a preference and lack user expressiveness. To tackle these issues, this paper presents a user preference model and mining framework for a context-aware m-services environment based on an intuitive quantitative preference measure and a strict partial order preference representation. Experimental evaluation of the user preference mining framework in a simulated m-Commerce environment showed that it is very promising. The preference mining algorithms were found to scale well with increases in the volumes of data.
Personalization and recommendation systems require a formalized model for user preference. We present the formal model of preference including positive preference and negative preference. For rare events, we apply the probability of random occurrence in order to reduce noise effects caused by data sparseness. Pareto distribution is adopted for the random occurrence probability. We also present the method for combining information of joint feature variables in different sizes by dynamic weighting using random occurrence probability.
Modeling user preference is one of the challenging issues in intelligent information systems. Extensive research has been performed to automatically analyze user preference and to utilize it. One problem still remains: The representation of preference, usually given by measure of vector similarity or probability, does not always correspond to common sense of preference. This problem gets worse in the case of negative preference. To overcome this problem, this paper presents a preference model using mutual information in a statistical framework. This paper also presents a method that combines information of joint features and alleviates problems arising from sparse data. Experimental results, compared with the previous recommendation models, show that the proposed model has the highest accuracy in recommendation tests.
We present here a formal foundation for an iterative and incremental approach to constructing and evaluating preference queries. Our main focus is on query modification: a query transformation approach which works by revising the preference relation in the query. We provide a detailed analysis of the cases where the order-theoretic properties of the preference relation are preserved by the revision. We consider a number of different revision operators: union, prioritized and Pareto composition. We also formulate algebraic laws that enable incremental evaluation of preference queries. Finally, we consider two variations of the basic framework: finite restrictions of preference relations and weak-order extensions of strict partial order preference relations.