Computing the web-user's relevance to a give topic is an important task for any personalization service on the Web. Since the interest and preference of a web-user are revealed in his Web browsing history, in this paper we develop a novel approach that utilizes Web logs to compute the relevance of a web-user to a given query. In contrast to traditional methods that are purely based on textual analysis, our approach calculates the web-user's relevance through link analysis under a unified framework where the importance of web-pages and web-users mutually reinforce each other in an iterative way. The experimental results show that our approach has achieved 53% of accuracy when ranking the web-user's relevance to a search topic.

Abstract. Since the introduction of the Enterprise Track at TREC in 2005, the task of finding experts has generated a lot of interest within the research community. Numerous models have been proposed that rank candidates by their level of expertise with respect to some topic. Common to all approaches is a component that estimates the strength of the association between a document and a person. Forming such associations, then, is a key ingredient in expertise search models. In this paper we introduce and compare a number of methods for building documentpeople associations. Moreover, we make underlying assumptions explicit, and examine two in detail: (i) independence of candidates, and (ii) frequency is an indication of strength. We show that our refined ways of estimating the strength of associations between people and documents leads to significant improvements over the state-of-the-art in the end-toend expert finding task. 1

Social network analysis has gained significant attention in recent years, largely due to the success of online social networking and media-sharing sites, and the consequent availability of a wealth of social network data. In spite of the growing interest, however, there is little understanding of the potential business applications of mining social networks. While there is a large body of research on different problems and methods for social network mining, there is a gap between the techniques developed by the research community and their deployment in real-world applications. Therefore the potential business impact of these techniques is still largely unexplored. In this article we use a business process classification framework to put the research topics in a business context and provide an overview of what we consider key problems and techniques in social network analysis and mining from the perspective of business applications. In particular, we discuss data acquisition and preparation, trust, expertise, community structure, network dynamics, and information propagation. In each case we present a brief overview of the problem, describe state-of-the art approaches, discuss business application examples, and map each of the topics to a business process classification framework. In addition, we provide insights on prospective business applications, challenges, and future research directions. The main contribution of this article is to provide a state-of-the-art overview of current techniques while providing a critical perspective on business applications of social network analysis and mining.

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Abstract. This paper proposes an agent community based information retrieval method, which uses agent communities to manage and look up information related to users. An agent works as a delegate of its user and searches for information that the user wants by communicating with other agents. The communication between agents is carried out in a peerto-peer computing architecture. In order to retrieve relevant information to a user query, an agent uses two histories: a query/retrieved document history(Q/RDH) and a query/sender agent history(Q/SAH). The former is a list of pairs of query and retrieved document information, where the queries were sent by the agent itself. The latter is a list of pairs of query and sender agents and shows “who sent what query to the agent”. This is useful to find a new information source. Making use of the Q/SAH is expected to cause a collaborative filtering effect, which gradually creates virtual agent communities, where agents with the same interests stay together. Our hypothesis is that a virtual agent community reduces communication loads to perform a search. As an agent receives more queries, then more links to new knowledge are achieved. From this behavior, a “give and take”(or positive feedback) effect for agents seems to emerge. We implemented this method with Multi-Agents Kodama, and conducted the experiments to test the hypothesis. The empirical results showed that the method was much more efficient than a naive method employing ’multicast ’ techniques only to look up a target agent. 1

Abstract. This paper presents an agent community based peer-to-peer information retrieval method called ACP2P method[16] and discusses the experimental results of the method. The ACP2P method uses agent communities to manage and look up information related to users. An agent works as a delegate of its user and searches for information that the user wants by communicating with other agents. The communication between agents is carried out in a peer-to-peer computing architecture. In order to retrieve information relevant to a user query, an agent uses a content file, which consists of retrieved documents and two histories: a query/retrieved document history(Q/RDH) and a query/sender agent history(Q/SAH). The former is a list of pairs of a query and the address of an agent that returned documents relevant to the query. The latter is a list of pairs of a query and the address of a sender agent and shows “who sent what query to the agent”. This is useful for finding a new information source. Making use of Q/SAH is expected to have a collaborative filtering effect, which gradually creates virtual agent communities, where agents with the same interests stay together. Our hypothesis is that a virtual agent community reduces communication loads necessary to perform a search. As an agent receives more queries, then more links to new knowledge are acquired. From this behavior, a “give and take”(or positive feedback) effect for agents seems to emerge. We implemented this method with Multi-Agent Kodama, and conducted experiments to test the hypothesis. The experimental results showed that the method employing two histories was much more efficient than a naive method employing ’multicast ’ techniques only to look up a target agent. Further, making use of Q/SAH facilitates bidirectional communications between agents and thus creates virtual agent communities. 1

This paper describes a prototype user interface system, CIRR tool, which allows searchers to share their relevance judgments, search queries, and search results by providing a system Task Console with a “Group Report ” feature. The system was developed based on the assumption that knowledge sharing in information retrieval (IR) is helpful and important to improve search performance. A usability evaluation of this system was conducted. The test results show that in general the system is easy to learn and use. However, some usability problems are also observed. The paper discusses these issues and proposes future research directions.

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Supporting the opportunistic learning that often occurs in organizations requires other approaches than traditional document centric knowledge management techniques and intelligent learning environments. Such opportunistic learning takes place in situations where (1) the learner is engaged in tasks/activities where there are no known solutions a priori, (2) where it is not easy to evaluate learning outcomes and (3) where the learning environment is dynamic: new information, processes, people are continuously evolving. This paper discusses this type of learning and describes how technologies for electronic documents could be used to augment an existing corporate memory framework that captures lessons learnt (L2Corp [Alem 98]) with a project memory and a tool for finding people. We argue that an intelligent organisational learning system needs to capture and represent the lessons learnt, needs to capture in a project memory the documents created and the electronic discussions between people, and should provide a means for finding experts within the organization. 1.

Abstract. One of the major problems for semantic search at Web scale is that the search results on the semantic data might be huge and the users have to browse to find the most relevant ones. Plus, due to the reason for the context, user requirement may diverse even the input query may be the same. In this paper, we try to achieve scalability in semantic search through social relation diversity of different users. Namely, we utilize one of the major context for users, social relations, to help refining the semantic search process. Social network based interest retention model is developed on top of user name based social relations, and is designed to be used in more wider range of Web scale semantic search tasks. The experiments are based on the SwetoDBLP dataset, and we can conclude that proposed method is potentially effective to help users find most relevant search results in a scalable environment.