Building User Communities of Interests by Using Latent Semantic Analysis

Nowadays Web users are facing the problems of information overload and drowning due to the significant and rapid growth in the amount of information and the large number of users. As a result, how to provide Web users more exactly needed information is becoming a critical issue in Web-based information retrieval and data management. In order to address the above difficulties, Web mining was proposed as an efficient means to discover the intrinsic relationships among Web data. In particular, Web usage mining is to discover Web usage patterns and utilize the discovered usage knowledge for constructing interest-oriented user communities, which could be, in turn, used for presenting Web users more personalized Web contents, i.e. Web recommendation. On the other hand, Latent Semantic Analysis (LSA) is one kind of approaches that is used to reveal the inherent correlation resided in co-occurrence activities, such as Web usage data. Moreover, LSA possesses the capability of capturing the hidden knowledge at semantic level that can’t be achieved by traditional methods. In this chapter, we aim to address building user communities of interests via combining Web usage mining and latent semantic analysis. Meanwhile we also present the application of user communities for Web recommendation.

Applications of a Social Software Model

This paper discusses social software technologies and presents an integrated social software model that can be used to achieve collective goals. This model has grown out of a need among practitioners to identify useful social software functionalities and to find out what to do with them. As the number of social software technologies increases, the question increasingly remains what to do with them and how to apply them usefully. The model can be used within an organization, to guide it in attaining organizational goals, but it can also be used to support activities in a network of organizations or in a network of non-affiliated individuals. First, we discuss social software in general. Subsequently, we discuss a model for understanding social software data and functionality. Finally, two possible applications of the social software model are discussed: knowledge sharing and increasing social inclusion among youth.

Managing Brief Data from Users to Professionals

Microblogging‘s explosion has provoked changes in the Blogosphere (now bloggers prefer to publish brief content in microblogs), and it has changed some roles in journalism too. Twitter is the most important tool in this phenomenon and it has served to keep connected sources, journalists and audiences. In recent years, media and news agencies are being characterized by an intensive use of microblogs. Journalists start to be collaborate within communities of interests trough microblogging, in particular Twitter. Facts like California fires are a clear example of Twitter coverages, which were started by users and gathered by journalists. This is more than a brief and fast tool for journalism, it is related with making connections with audiences, witnesses and sources of breaking news. In this sense, this chapter will show several examples in order to explain how Twitter is a new way to design collaborative coverages. Hence, it is not just a platform on fashion.

P2P Semantic Coordination for Collective Knowledge Organization

In this chapter, we present a P2P coordination approach for setting up and exploiting collective peer knowledge provided by autonomously emerging semantic communities. This approach aims at providing a practical means for allowing a peer to move from a restricted peer knowledge space, where it is considered as a single agent with its personal knowledge, towards an intermediate collective knowledge space, where it is considered as a member of a community storing a part of the overall collective knowledge, up to a final collective peer-knowledge space, where the peer builds its personal and coordinated view of the collective knowledge of interest harvested from the underlying communities. In this respect, ontologies and Semantic matching techniques are exploited to set up collective knowledge and to effectively enforce distributed resource sharing.

Collective Information Filtering for Web Observatories

Web observatories are becoming a common on-line practice. Their role is to compile, organize and convey information that serves the needs of a thematically focused Web community. So far they are typically following a centralized approach, with an editorial team being responsible for finding, collecting, editing and presenting the observatory‘s information content. We propose a new approach for the development of Web observatories based on Collective Information Filtering. Community profiles are used to capture the collective interests of community members and evaluate the relevance of information content accordingly. We can thus build Web observatories that can be dynamically enriched and can continuously adapt their content to the interests/needs of the observatory‘s community. This new approach not only reduces significantly the cost of developing and maintaining a Web observatory, but also, following the current Web trends, it is community driven. In this chapter, we discuss Collective Information Filtering and we describe the architecture for applying it to a Web Observatory. We also present a series of prototype Web Observatories that adopt the proposed approach.

A Semi-Supervised Algorithm to Manage Communities of Interests

This chapter presents a genetic algorithm, called the Similarity-based Clustering Genetic Algorithm (SCGA), used to group users‘ profiles. This algorithm is integrated in an approach which allows to share documents among users browsing a collection of documents. The users are described in terms of profiles, with each profile corresponding to one area of interest. While browsing through the collection of documents, users‘ profiles are computed. These profiles are then grouped into communities of interests using the SCGA which is based on the Grouping Genetic Algorithm (GGA). In fact, the SCGA can solve other similar problems under certain circumstances. The approach is part of a more generic model to manage information called the GALILEI Framework. This framework, which provides promising results, has been developed in a software platform available under the GNU GPL license.

People Company

In this chapter we present results of our research on a collaborative platform that enables employees of a global company to present themselves, their business and company site in a company-wide autograph book. For the content generation the employees received an innovative technology, an application running on an Ultra-Mobile Personal Computer (UMPC) that enables users to generate video, sound, simple text, drawings, and photos. Main goal of this applied research is to bridge the gap between the various company sites, to foster working relationships and to strengthen the common understanding that each employee is part of a people company. This chapter describes the application, it presents an analysis of the generated content, the evaluation of the users’ acceptance of the UMPC application and the autograph book and finally an outlook on further research activities informed by these results.

Collaborative Recommendation Systems and Link Analysis

Link analysis is a framework usually associated with fields such as graph mining, relational learning, Web mining, text mining, hyper-text mining, visualization of link structures. It provides and analyzes relationships and associations between many objects of various types that are not apparent from isolated pieces of information. This chapter shows how to apply various link-analysis algorithms exploiting the graph structure of databases on collaborative-recommendation tasks. More precisely, two kinds of link-analysis algorithms are applied to recommend items to users: random-walk based models and kernel-based models. These link-analysis based algorithms do not use any feature of the items in order to compute the recommendations, they first compute a matrix containing the links between persons and items, and then derive recommendations from this matrix or part of it.

Articulating Tacit Knowledge in Multinational E-Collaboration on New Product Designs

An e-business new product development (NPD) knowledge articulation model is built from the interdisciplinary empirical and theoretical literature. The model is intended to facilitate a case study of a large multinational mobile communications services/products company (with team members in Europe, Asia and Australia). The NPD teams include subject matter experts that function as a community of practice, electronically collaborating in a virtual context. The knowledge created and shared in the NPD teams involve various unknown levels of tacit and explicit ideas, which are difficult to understand or assess. The goal of the research is to build a tacit knowledge articulation framework and measurement construct that can be used to understand how a successful (or unsuccessful) NPD team operates, in terms of knowledge innovation and productivity. Complex issues and controversies in knowledge management are examined to clarify terminology for future research.