The Deconstructed (or Distributed) Journal - an emerging model?

Reviews the development of the Deconstructed Journal academic publishing model. The model was first proposed in something like its present form in 1997 and further developed in 1999. Although not actively promoted elements of the model appear to be emerging spontaneously from the general developments in online academic publishing

Intelligent Personalized Searching

Search engine is a very useful tool for almost everyone nowadays. People use search engine for the purpose of searching about their personal finance, restaurants, electronic products, and travel information, to name a few. As helpful as search engines are in terms of providing information, they can also manipulate people behaviors because most people trust online information without a doubt. Furthermore, ordinary users usually only pay attention the highest-ranking pages from the search results. Knowing this predictable user behavior, search engine providers such as Google and Yahoo take advantage and use it as a tool for them to generate profit. Search engine providers are enterprise companies with the goal to generate profit, and an easy way for them to do so is by ranking up particular web pages to promote the product or services of their own or their paid customers. The results from search engine could be misleading. The goal of this project is to filter the bias from search results and provide best matches on behalf of users’ interest

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N

Learning users' interests by quality classification in market-based recommender systems

Recommender systems are widely used to cope with the problem of information overload and, to date, many recommendation methods have been developed. However, no one technique is best for all users in all situations. To combat this, we have previously developed a market-based recommender system that allows multiple agents (each representing a different recommendation method or system) to compete with one another to present their best recommendations to the user. In our system, the marketplace encourages good recommendations by rewarding the corresponding agents who supplied them according to the users’ ratings of their suggestions. Moreover, we have theoretically shown how our system incentivises the agents to bid in a manner that ensures only the best recommendations are presented. To do this effectively in practice, however, each agent needs to be able to classify its recommendations into different internal quality levels, learn the users’ interests for these different levels, and then adapt its bidding behaviour for the various levels accordingly. To this end, in this paper we develop a reinforcement learning and Boltzmann exploration strategy that the recommending agents can exploit for these tasks. We then demonstrate that this strategy does indeed help the agents to effectively obtain information about the users’ interests which, in turn, speeds up the market convergence and enables the system to rapidly highlight the best recommendations

Contribution structures

The invisibility of the individuals and groups that gave rise to requirements artifacts has been identified as a primary reason for the persistence of requirements traceability problems. This paper presents an approach, based on modelling the dynamic contribution structures underlying requirements artifacts, which addresses this issue. We show how these structures can be defined, using information about the agents who have contributed to artifact production, in conjunction with details of the numerous traceability relations that hold within and between artifacts themselves. We describe a scheme, derived from work in sociolinguistics, which can be used to indicate the capacities in which agents contribute. We then show how this information can be used to infer details about the social roles and commitments of agents with respect to their various contributions and to each other. We further propose a categorisation for artifact-based traceability relations and illustrate how they impinge on the identification and definition of these structures. Finally, we outline how this approach can be implemented and supported by tools, explain the means by which requirements change can be accommodated in the corresponding contribution structures, and demonstrate the potential it provides for "personnel-based" requirements traceability