Deep Learning Framework for Online Interactive Service Recommendation in Iterative Mashup Development

Recent years have witnessed the rapid development of service-oriented computing technologies. The boom of Web services increases the selection burden of software developers in developing service-based systems (such as mashups). How to recommend suitable follow-up component services to develop new mashups has become a fundamental problem in service-oriented software engineering. Most of the existing service recommendation approaches are designed for mashup development in the single-round recommendation scenario. It is hard for them to update recommendation results in time according to developers' requirements and behaviors (e.g., instant service selection). To address this issue, we propose a deep-learning-based interactive service recommendation framework named DLISR, which aims to capture the interactions among the target mashup, selected services, and the next service to recommend. Moreover, an attention mechanism is employed in DLISR to weigh selected services when recommending the next service. We also design two separate models for learning interactions from the perspectives of content information and historical invocation information, respectively, as well as a hybrid model called HISR. Experiments on a real-world dataset indicate that HISR outperforms several state-of-the-art service recommendation methods in the online interactive scenario for developing new mashups iteratively.Comment: 15 pages, 6 figures, and 3 table

ESPON Project 2.3.1., Application and effects of the ESDP in the Member States. Second Interim Report

This Second Interim Report includes the preliminary results of the project, "Application and Effects of the ESDP in the Member States" within the ESPON Programme 2000-2006. The focus of the study is the application of the European Spatial Development Perspective (ESDP), which was adopted at the Potsdam informal Ministerial Council meeting in May 199

Project Triton : A study into delivering targeted information to an individual based on implicit and explicit data.

The World Wide Web is frequently seen as a source of knowledge, however much of this remains undiscovered by its users. In recent times, recommender systems (e.g. Digg and Last.fm) have attempted to bridge this gap, alerting users to previously untapped knowledge. As more socially oriented services appear on the Web (e.g. Facebook and MySpace), it has never been easier to obtain information pertaining to an individual’s interests. At present, solutions for automated data recommendation tend to be highly topic specific (recommending only a certain topic such as news) and often only allow access to the system using monolithic interfaces. This report hopes to detail the stages from research to evaluation involved in creating an extensible framework, which will operate without the need for human intervention. The framework will feature several proof-of-concept plugins residing in a custom workflow, which target information that is useful to the user. Information will be retrieved automatically through plugins involved with data gathering (such as feed processing and page scraping), while users’ interests will be obtained implicitly (for example, using header information to derive location) or explicitly (taking advantage of Social Network APIs such as Facebook Connect). Finally, Third Parties will be able to integrate the framework into their own solutions using the customisable XML API (written in PHP), so that their products can provide custom user interfaces without style constraints

From planning the port/city to planning the port-city : exploring the economic interface in European port cities

In last three decades, planning agencies of most ports have institutionally evolved into a (semi-) independent port authority. The rationale behind this process is that port authorities are able to react more quickly to changing logistical and spatial preferences of maritime firms, hence increasing the competitiveness of ports. Although these dedicated port authorities have proven to be largely successful, new economic, social, and environmental challenges are quickly catching up on these port governance models, and particularly leads to (spatial) policy ‘conflicts’ between port and city. This chapter starts by assessing this conflict and argue that the conflict is partly a result of dominant—often also academic—spatial representations of the port city as two separate entities. To escape this divisive conception of contemporary port cities, this chapter presents a relational visualisation method that is able to analyse the economic interface between port and city. Based on our results, we reflect back on our proposition and argue that the core challenge today for researchers and policy makers is acknowledging the bias of port/city, being arguably a self-fulfilling prophecy. Hence, we turn the idea of (planning the) port/city conflicts into planning the port-city’s strengths and weaknesses