Collaborative filtering via sparse Markov random fields

Recommender systems play a central role in providing individualized access to information and services. This paper focuses on collaborative filtering, an approach that exploits the shared structure among mind-liked users and similar items. In particular, we focus on a formal probabilistic framework known as Markov random fields (MRF). We address the open problem of structure learning and introduce a sparsity-inducing algorithm to automatically estimate the interaction structures between users and between items. Item-item and user-user correlation networks are obtained as a by-product. Large-scale experiments on movie recommendation and date matching datasets demonstrate the power of the proposed method.

Linking and leveraging resources for innovation and growth through collaborative value creation: a study of Indian OSPs

How do knowledge-intensive and technology-based service providers from emerging economies sustain their innovation to grow rapidly and become dominant global players in their fields? We explain this recent phenomenon for a sample of offshore service providers (OSPs) from India by drawing from the collaborative value creation theoretical perspective and Mathews’ (Asia Pacific Journal of Management, 23(1):5–27, 2006a) resource “linking and leveraging” concepts. This paper shows that OSPs from India develop high quality relationships to enable them access and exploit network resources in delivering customized and innovative services to clients globally. The findings of this research provide further evidence that Mathews’ (Asia Pacific Journal of Management, 23(1):5–27, 2006a) internationalization framework of emerging economy multinational enterprises can be generalized to service providers.

An automated collaborative requirements engineering tool for better validation of requirements

This demo introduces an automated collaborative requirements engineering tool, called TestMEReq, which is used to promote effective communication and collaboration between client-stakeholders and requirements engineers for better requirements validation. Our tool is augmented with real time communication and collaboration support to allow multiple stakeholders to collaboratively validate the same set of requirements. We have conducted a user study focusing on validating requirements using TestMEReq with a few groups of requirements engineers and client stakeholders. The study shows that our automated tool support is able to assist requirements engineers to effectively communicate with client-stakeholders to better validate the requirements virtually in real time. (Demo video: https://www.youtube.com/watch?v=7sWLOx-N4Jo).

Review: 'Collaborative projects: An interdisciplinary study'

When I was asked to review this book, I was immediately intrigued by the title. As someone influenced by the philosophical legacy of phenomenology and existential humanism through Husserl, Heidegger and Sartre, I have always understood the project as a life-project of which I am the author seeking to overcome the facticity, or the inescapable conditions of my existence, in order to realise myself in the world. Such a project takes courage: in pro - jecting myself into the world I simultaneously find and lose myself, but without this attempt I do not exist at all. In recent years I have come to increasingly question this account of the human project. Could the idea of ‘collaborative projects’ be a more positive way of seeing the social world in which I live, as a world where other people are not simply an obstacle to my self-realisation?

INNOVATIVE HUMAN-COMPUTER INTERACTIONS TO SUPPORT COGNITIVE COLLABORATIVE GEOSPATIAL ENVIRONMENTS

In the current world geospatial information is being demanded in almost real time, which requires the speed at which this data is processed and made available to the user to be at an all-time high. In order to keep up with this ever increasing speed, analysts must find ways to increase their productivity. At the same time the demand for new analysts is high, and current methods of training are long and can be costly. Through the use of human computer interactions and basic networking systems, this paper explores new ways to increase efficiency in data processing and analyst training.

A COMPARATIVE STUDY AND EVALUATION OF COLLABORATIVE RECOMMENDATION SYSTEMS

Consumers currently enjoy a surplus of goods (books, videos, music, or other items) available to purchase. While this surplus often allows a consumer to find a product tailored to their preferences or needs, the volume of items available may require considerable time or effort on the part of the user to find the most relevant item. Recommendation systems have become a common part of many online business that supply users books, videos, music, or other items to consumers. These systems attempt to provide assistance to consumers in finding the items that fit their preferences. This report presents an overview of recommendation systems. We will also briefly explore the history of recommendation systems and the large boost that was given to research in this field due to the Netflix Challenge. The classical methods for collaborative recommendation systems are reviewed and implemented, and an examination is performed contrasting the complexity and performance among the various models. Finally, current challenges and approaches are discussed.

A Collaborative Mobile Crowdsensing System for Smart Cities

Nowadays words like Smart City, Internet of Things, Environmental Awareness surround us with the growing interest of Computer Science and Engineering communities. Services supporting these paradigms are definitely based on large amounts of sensed data, which, once obtained and gathered, need to be analyzed in order to build maps, infer patterns, extract useful information. Everything is done in order to achieve a better quality of life. Traditional sensing techniques, like Wired or Wireless Sensor Network, need an intensive usage of distributed sensors to acquire real-world conditions. We propose SenSquare, a Crowdsensing approach based on smartphones and a central coordination server for time-and-space homogeneous data collecting. SenSquare relies on technologies such as CoAP lightweight protocol, Geofencing and the Military Grid Reference System.

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

Collaborative filtering and deep learning based hybrid recommendation for cold start problem

Recommender systems (RS) are used by many social networking applications and online e-commercial services. Collaborative filtering (CF) is one of the most popular approaches used for RS. However traditional CF approach suffers from sparsity and cold start problems. In this paper, we propose a hybrid recommendation model to address the cold start problem, which explores the item content features learned from a deep learning neural network and applies them to the timeSVD++ CF model. Extensive experiments are run on a large Netflix rating dataset for movies. Experiment results show that the proposed hybrid recommendation model provides a good prediction for cold start items, and performs better than four existing recommendation models for rating of non-cold start items.

The rise and fall of Kt/V : lessons of evidence for dialysis nursing

Editorial piece from the July 2015 issue of Renal Society of Australasia Journal.

Ghostly sisters: feminist collaborative performance in Australia

This article examines how feminist performance has been, and continues to be, a key vehicle for the collaborative exploration of sexual difference and female subjectivity in Australia. It focuses specifically on the Lean Sisters and Generic Ghosts, whose collaborative performances occurred during the seventies and eighties, and their impact on subsequent feminist collaborative performance groups. As the article demonstrates, this counter-cultural tradition of performance typically deploys tactics of intertextuality, cross-media experimentation, humour, and détournement to critique gender oppression and its recurrence, while staging new possibilities of an embodied feminist politics.