2852278
An Adaptive Recommendation System without Explicit Acquisition of User Relevance Feedback
Recommendation systems are widely adopted in e-commerce businesses for helping customers locate products they would like to purchase. In an earlier work, we introduced a recommendation system, termed Yoda, which employs a hybrid approach that combines collaborative filtering (CF) and content-based querying to achieve higher accuracy for large-scale Web-based applications. To reduce the complexity of the hybrid approach, Yoda is structured as a tunable model that is trained off-line and employed for real-time recommendation on-line. The on-line process benefits from an optimized aggregation function with low complexity that allows the real-time aggregation based on confidence values of an active user to pre-defined sets of recommendations. In this paper, we extend Yoda to include more recommendation sets. The recommendation sets can be obtained from different sources, such as human experts, web navigation patterns, and clusters of user evaluations.More over, the extended Yoda can learn the confidence values automatically by utilizing implicit users' relevance feedback through web navigations using genetic algorithms (GA). Our end-to-end experiments show while Yoda's complexity is low and remains constant as the number of users and/or items grow, its accuracy surpasses that of the basic nearest-neighbor method by a wide margin (in most cases more than 100%). The experimental results also indicate that the retrieval accuracy is significantly increased by using the GA-based learning mechanism.
17575068
Yoda, an adaptive soft classification model: content-based similarity queries and beyond
Abstract. Providing a customized result set based upon a user preference is the ultimate objective of many content-based image retrieval systems. There are two main challenges in meeting this objective: First, there is a gap between the physical characteristics of digital images and the semantic meaning of the images. Secondly, different people may have different perceptions on the same set of images. To address both these challenges, we propose a model, named Yoda, that conceptualizes content-based querying as the task of soft classifying images into classes. These classes can overlap, and their members are different for different users. The “soft” classification is hence performed for each and every image feature, including both physical and semantic features. Subsequently, each image will be ranked based on the weighted aggregation of its classification memberships. The weights are user-dependent, and hence different users would obtain different result sets for the same query. Yoda employs a fuzzy-logic based aggregation function for ranking images. We show that, in addition to some performance benefits, fuzzy aggregation is less sensitive to noise and can support disjunctive queries as compared to weighted-average aggregation used by other content-based image retrieval systems. Finally, since Yoda heavily relies on user-dependent weights (i.e., user profiles) for the aggregation task, we utilize the users' relevance feedback to improve the profiles using genetic algorithms (GA). Our learning mechanism requires fewer user interactions, and results in a faster convergence to the user's preferences as compared to other learning techniques.
2628080
Web Information Personalization: Challenges and Approaches
As the number of web pages increases dramatically, the problem of the information overload becomes more severe when browsing and searching the WWW. To alleviate this problem, personalization becomes a popular remedy to customize the Web environment towards a user’s preference. To date, recommendation systems and personalized web search systems are the most successful examples of Web personalization. By focusing on these two types of systems, this paper reviews the challenges and the corresponding approaches proposed in the past ten years.
39358979
Multi-agent based dynamic scheduling for a flexible assembly system
This paper proposes a multi-agent based dynamic scheduling approach for a flexible assembly system. We first introduce a flexible control system developed by Intelligent Robotics and Automation Laboratory in National Taiwan University. Based on that control system, the agents can communicate with each other conveniently. A generic agent architecture is proposed to model the pieces of equipment in the flexible assembly system. With a distributed architecture, the agents make their scheduling decisions using their local rule base. The agents acquire the resources following the distributed resource allocation protocol. The scheduling complexity is reduced to meet the real-time response requirement in the applications for flexible automated production. The present work is applied to the experimental robotized flexible assembly system in the above laboratory.
20353418
Perspectives on Service-Oriented Computing and Service-Oriented System Engineering
The service-oriented computing (SOC) paradigm leads the innovation of today's computing system and software development, and it can transform the human society into a new republic of all-to-all connected world. There are a number of interesting and innovative research challenges involved in achieving this vision of service-oriented system engineering (SOSE). This paper highlights some of these significant research opportunities and also the need for enhanced education in this emerging area
14712495
One-class SVM for learning in image retrieval
Relevance feedback schemes using linear/quadratic estimators have been applied in content-based image retrieval to improve retrieval performance significantly. One major difficulty in relevance feedback is to estimate the support of target images in high dimensional feature space with a relatively small number of training samples. We develop a novel scheme based on one-class SVM, which fits a tight hyper-sphere in the nonlinearly transformed feature space to include most of the target images based on positive examples. The use of a kernel provides us an elegant way to deal with nonlinearity in the distribution of the target images, while the regularization term in SVM provides good generalization ability. To validate the efficacy of the proposed approach, we test it on both synthesized data and real-world images. Promising results are achieved in both cases.
10348131
An Infrastructure for Web Services Migration for Real-Time Applications
Service-oriented architecture (SOA) is a highly promising technology for software development. It can greatly reduce the development time and cost and achieve system agility. Web service is the current practice for realizing SOA. Due to the nature of widely distributed Web service providers, the service performance could be impacted when the network traffic is congested. There are methods, such as communication cost based service selection strategies, developed to cope with the problem. In this paper, we propose a complementing technology, migratable Web services, to overcome the performance problems for real-time applications. A service can be implemented by multiple light weighted Web services and some of them can be migrated to strategic sites to minimize the communication delays so the real-time requirements can be met. We have designed an infrastructure to support Web service migration and execution. Also, appropriate decision mechanism, which decides whether a Web service should be migrated and where to send it to, is the key for major performance gains. We use genetic algorithm to efficiently make the best migration decisions. Experimental studies show that the approach can achieve significant performance improvement
8394681
ViteX: a streaming XPath processing system
We present ViteX, an XPath processing system on XML streams with polynomial time complexity. ViteX uses a polynomial-space data structure to encode an exponential number of pattern matches (in the query size) which are required to process queries correctly during a single sequential scan of XML. Then ViteX computes query solutions by probing the data structure in a lazy fashion without enumerating pattern matches.