Community identification algorithms have been used to enhance the quality of the services perceived by its users. Although algorithms for community have a widespread use in the Web, their application to portals or specific subsets of the Web has not been much studied. In this paper, we propose a technique for local community identification that takes into account user access behavior derived from access logs of servers in the Web. The technique takes a departure from the existing community algorithms since it changes the focus of in terest, moving from authors to users. Our approach does not use relations imposed by authors (e.g. hyperlinks in the case of Web pages). It uses information derived from user accesses to a service in order to infer relationships. The communities identified are of great interest to content providers since they can be used to improve quality of their services. We also propose an evaluation methodology for analyzing the results obtained by the algorithm. We present two case studies based on actual data from two services: an online bookstore and an online radio. The case of the online radio is particularly relevant, because it emphasizes the contribution of the proposed algorithm to find out communities in an environment (i.e., streaming media service) without links, that represent the relations imposed by authors (e.g. hyperlinks in the case of Web pages).
Besides, we are considering community identification applied to a local context instead of the whole Web. Our approach aims to adapt the graph based community identification algorithm described in @cite_5 . Some modifications to @cite_5 that takes into account user information have already been proposed in @cite_10 . However, this work was not focused on the community identification capabilities of @cite_5 and also considered a different representation of user patterns.
@cite_5 @cite_10
2020423193 1968509530
The World Wide Web grows through a decentralized, almost anarchic process, and this has resulted in a large hyperlinked corpus without the kind of logical organization that can be built into more tradit,ionally-created hypermedia. To extract, meaningful structure under such circumstances, we develop a notion of hyperlinked communities on the www t,hrough an analysis of the link topology. By invoking a simple, mathematically clean method for defining and exposing the structure of these communities, we are able to derive a number of themes: The communities can be viewed as containing a core of central, “authoritative” pages linked togh and they exhibit a natural type of hierarchical topic generalization that can be inferred directly from the pat,t,ern of linkage. Our investigation shows that although the process by which users of the Web create pages and links is very difficult to understand at a “local” level, it results in a much greater degree of orderly high-level structure than has typically been assumed. Kleinberg’s HITS algorithm, a method of link analysis, uses the link structure of a network of webpages to assign authority and hub weights to each page. These weights are used to rank sources on a particular topic. We have found that certain tree-like web structures can lead the HITS algorithm to return either arbitrary or non-intuitive results. We give a characterization of these web structures. We present two modifications to the adjacency matrix input to the HITS algorithm. Exponentiated Input, our first modification, includes information not only on direct links but also on longer paths between pages. It resolves both limitations mentioned above. Usage Weighted Input, our second modification, weights links according to how often they were followed by users in a given time period; it incorporates user feedback without requiring direct user querying.