International Journal of Web Engineering and Technology

Giannikopoulos, Panagiotis ; Vassilakis, Costas

2012 International Journal of Web Engineering and Technology

doi: 10.1504/IJWET.2012.048517

In contemporary internet architectures, including server farms and blog aggregators, web log data may be scattered among multiple cooperating peers. In order to perform content personalisation through provision of recommendations on such architectures, it is necessary to employ a recommendation algorithm; however, the majority of such algorithms are centralised, necessitating excessive data transfers and exhibiting performance issues when the number of users or the volume of data increase. In this paper, we propose an approach where the clickstream information is distributed to a number of peers, which cooperate for discovering frequent patterns and for generating recommendations, introducing: The proposed approach may be employed in various domains, including digital libraries, social data, server farms and content distribution networks.

Tsai, Flora S.

2012 International Journal of Web Engineering and Technology

doi: 10.1504/IJWET.2012.048519

The explosive growth of blogs creates a critical demand for information retrieval techniques to effectively search for required and meaningful information. This paper studied the blog distillation and feed search task in the TREC Blog Track, which was designed to search for the relevant feeds which have a principal and recurring interest in a particular topic or query. In this paper, a novel data-centric approach is proposed which achieved good results compared to others in the TREC Blog Track.

Varlamis, Iraklis ; Tsatsaronis, George

2012 International Journal of Web Engineering and Technology

doi: 10.1504/IJWET.2012.048520

Bibliographic databases are a prosperous field for data mining research and social network analysis. They contain rich information, which can be analysed across different dimensions (e.g., author, year, venue, and topic) and can be exploited in multiple ways. The representation and visualisation of bibliographic databases as graphs and the application of data mining techniques can help us uncover interesting knowledge concerning potential synergies between researchers, possible matchings between researchers and venues, candidate reviewers for a paper or even the ideal venue for presenting a research work. In this paper, we propose a novel representation model for bibliographic data, which combines co-authorship and content similarity information, and allows for the formation of scientific networks. Using a graph visualisation tool from the biological domain, we are able to provide comprehensive visualisations that help us uncover hidden relations between authors and suggest potential synergies between researchers or groups.

Yamaguchi, Yuto ; Amagasa, Toshiyuki ; Kitagawa, Hiroyuki

2012 International Journal of Web Engineering and Technology

doi: 10.1504/IJWET.2012.048526

This paper addresses the problem of tagging users in Twitter, one of the most popular microblogs. Although there are an enormous number of Twitter users, some are particularly influential regarding certain topics (e.g., politics, sports). These users often transmit useful information about their topics. For example, a user familiar with political issues often transmits useful information about the latest political news. To obtain useful information, therefore, it is very important to know these user topics. To discover user topics, we propose a user tagging method using Twitter lists, the official Twitter function for making and sharing user lists. From our observations, users included in the same list were likely to have posted on the same topic. This topic was often described by the list name. For example, the list named ‘politicians-list’ has politicians as its members. For this reason, our proposed method regards list names as sequences of tags and assigns them to list members. Experiments conducted using two datasets showed that our proposed method works effectively in the user profiling domain and the user ranking domain.