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Organizational software processes improvement offers a key opportunity for organizations to be more efficient and effective. However, in most cases software process improvement initiatives in organizations become a path full of obstacles. As a consequence, organizations worldwide are adopting international standards and models in an effort to improve their software processes. Nevertheless, the difficulty of using multi-model improvement environments for the implementation of software processes improvements is well-known. In this context, effective integration of models and standards is a tool that enhances information sharing and communication. Integration can play a crucial role for the implementation of multi-model environments in order to be used as a reference model in software process improvements. This paper shows a methodology that enables organizations to establish a multi-model environment based on their business goals and on the results of a case study focused on implementing improvements in project management processes. Finally, results obtained after applying the methodology and future research line related to a platform for providing knowledge management and information sharing are included.

Latent Factor Model (LFM) based approaches are becoming popular when implementing Collaborative Filtering (CF) recommenders, due to their high recommendation accuracy. However, current LFM approaches address the accuracy issue only based on the rating data, whereas early research indicates that integrating information from additional data sources is helpful to the recommendation accuracy. In this work we focus on improving the recommendation accuracy of a LFM based CF recommender by integrating folksonomy information. To implement this approach, we first propose a novel model named Item Folsonomy Relevance (IFR) to analyze the item relevance inside the folksonomy; we subsequently integrate the latent factors of the IFR model and rating data through probabilistic matrix factorization (PMF), a state-of-the-art matrix factorization technique, to produce recommendations based on information from both the ratings and folksonomy simultaneously. The experiments on MovieLens dataset showed that compared to two state-of-the-art LFM approaches and another folksonomy-augmented recommder, our approach could obtain advantage in recommendation accuracy.

A distributed classification paradigm known as collaborative tagging has been widely adopted in new Web applications designed to manage and share online resources. Users of these applications organize resources (Web pages, digital photographs, academic papers) by associating with them freely chosen text labels, or tags. Here we leverage the social aspects of collaborative tagging and introduce a notion of resource distance based on the collective tagging activity of users. We collect data from a popular system and perform experiments showing that our definition of distance can be used to build a weighted network of resources with a detectable community structure. We show that this community structure clearly exposes the semantic relations among resources. The communities of resources that we observe are a genuinely emergent feature, resulting from the uncoordinated activity of a large number of users, and their detection paves the way for mapping emergent semantics in social tagging systems.

Tags are widely used in Web 2.0 applications, such as blogs and online bookmarks. Many characteristics of tags have been studied in previous works, but the granularity level of tags still remains untouched. Granularity level describes how focused a tag is, the lower the level, the more concrete concept the tag covers. The paper has three main contributions: 1) We propose a method to discover the concept level of tags automatically. 2) We apply the method to a real-world tag data set, and study the distribution of levels. 3) We perform tag recommendation concerning their concept levels. Experiments are conducted on a large-scale real world Chinese blog data set. We find that most tags are in more concrete levels, and the users tend to choose tags within the same concept level for a document. When predicting tags for a new document, tags in higher concept levels provide little help, yet may hurt the performance.

There are millions of digital images available and their use has been increasing. Image users are very diverse, from professional users to users searching for fun. An image can be seen and described in a different way by different users. What the user sees depends on his perception and cognitive processes. The purpose of this paper is to examine how users describe images. Students of the University of Ljubljana are surveyed on a random sample of images taken from Digital Library of Slovenia and Flickr in order to assign tags to each image. The tags are grouped into categories of image attributes such as objects, place, time, etc. and compared to tags assigned by Slovene library professionals and Flickr visitors. Finally, the obtained data are investigated to ascertain whether differences in tagging practices exist among students, library professionals and Flickr users.