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In this paper, we propose a novel community-based approach for web service selection where super-agents with more capabilities serve as community managers. They maintain communities and build community-based reputation for a service based on the opinions from all community members that have similar interests and judgement criteria. The community-based reputation is useful for consumer agents in selecting...
Designing personalized search engines based on a recommender system that takes into consideration the user situated moment in relation to the subject matter and the context that governs user interest has been largely ignored. In this paper, we present a novel approach to integrating user interests into search within a recommender system that is guided by the semantic representation of the user and...
Recent years have seen a huge increase in the amount of publicly-available information relevant to drug discovery, including online databases of compound and bioassay information; scholarly publications linking compounds with genes, targets and diseases; and predictive models that can suggest new links between compounds, genes, targets and diseases. However, there is a lack of tools and methods to...
Latent relational search is a new search paradigm based on the degree of analogy between two word pairs. A latent relational search engine is expected to return the word Paris as an answer to the question mark (?) in the query {(Japan, Tokyo), (France, ?)} because the relation between Japan and Tokyo is highly similar to that between France and Paris. We propose an approach for exploring and indexing...
Search engines have greatly influenced the way people access information on the Internet as such engines provide the preferred entry point to billions of pages on the Web. Therefore, highly ranked web pages generally have higher visibility to people and pushing the ranking higher has become the top priority for webmasters. As a matter of fact, search engine optimization (SEO) has became a sizeable...
In this paper, we propose a novel method combined classical collaborative filtering (CF) and bipartite network structure. Different from the classical CF, user similarity is viewed as personal recommendation power and during the recommendation process, it will be redistributed to different users. Furthermore, a free parameter is introduced to tune the contribution of the user to the user similarity...
Personalization, a topmost concern of modern recommendation systems (RS), is intended to predict individual motivation of a customer for this or that choice. It depends on many factors forming explicit and implicit decision context. The paper proposes RS personalization technology that focuses on ontology-based extraction of semantically interpretable context of each particular customer's decisions...
This paper presents a new type of digital repositories based on a hybrid organizational structure that leverages the potential of domain specific collaborative tagging in combination with a taxonomy-driven classification. It is exemplified in Linked Course - a repository prototype for collaborative development, sharing and reuse of learning resources equipped with adequate searching tools. The focus...
To solve the problem of searching for an optimal elimination ordering of Bayesian networks, a novel effective heuristic, MinSum Weight, and an ACS approach incorporated with multi-heuristic mechanism are proposed. The ACS approach named MHC-ACS utilizes a set of heuristics to direct the ants moving in the search space. The cooperation of multiple heuristics helps ants explore more regions. Moreover,...
As there are more and more online sources available on the Web, it becomes very time-consuming, if not impossible, to visit and search all web sites, one by one. Many search engines has been developed to help users find information of their need. However, search engines work poor for online sources whose data are often in deep web, which is not part of surface web indexed by standard search engines...
User-generated reviews play an important role for potential consumers in making purchase decisions. However, the quality and helpfulness of user-generated reviews are unavailable unless consumers read through them. Automatically predicting the helpfulness of user-generated reviews can assist consumers in discovering helpful reviews. Existing helpfulness assessing models make use of the positive vote...
This article deals with the issue of concept learning and tries to have a game theoretic view over the process of cooperative concept learning among agents in a multi-agent system, in which an extreme sense of competition has arisen. This gives birth to a new realm labeled as ”Learning Games”. We study the cooperative view and give a novel idea to use in competitive environments based on the solution...
Auditing Information Systems Security is difficult and becomes crucial to ensure the daily operational activities of organizations as well as to promote competition and to create new business opportunities. A conceptual security framework to manage and audit Information System Security is proposed and discussed. The proposed framework is based on a conceptual model approach, based on the ISO/IEC_JCT1...
Social network analysis (SNA) has attracted a lot of attention over the past years. Existing tools for SNA do not allow a user-centric analysis of the social neighborhood, i.e., the subgraph of the user's friends and friends of a friend. In this paper, we introduce SONAR, an open source Web application for user-centric SNA. Its extensible architecture and flexible data model allows developers to embed...
A user's cognitive style has been found to affect how they search for information, how they analyze the information, and how they make decisions in an analytical process. In this paper, we propose an approach that uses Hidden Markov Models (HMM) to dynamically capture a user's cognitive style by automatically exploring the sequence of actions and relevant information with respect to the content of...
The performance of many emerging communication paradigms depend on high levels of cooperation amongst the peers in the network. Although an individual's best strategy may be to selfishly consume resources without reciprocation, the optimal social performance requires agents in the network to behave in an altruistic manner. This paper considers a P2P data dissemination scenario, and applies an autonomic...
Significant increase in collected data for investigative tasks and the increased complexity of the reasoning process itself have made investigative analytical tasks more challenging. These tasks are time critical and typically involve identifying and tracking multiple hypotheses; gathering evidence to validate the correct hypotheses and eliminating the incorrect ones. In this paper we specifically...
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