94,335 research outputs found
ICoNOs MM: The IT-enabled Collaborative Networked Organizations Maturity Model
The focus of this paper is to introduce a comprehensive model for assessing and improving maturity of business-IT alignment (B-ITa) in collaborative networked organizations (CNOs): the ICoNOs MM. This two dimensional maturity model (MM) addresses five levels of maturity as well as four domains to which these levels apply: partnering structure, information system (IS) architecture, process architecture and coordination. The model can be used to benchmark and support continuous improvement of B-ITa process areas in CNOs
Algorithms and Architecture for Real-time Recommendations at News UK
Recommendation systems are recognised as being hugely important in industry,
and the area is now well understood. At News UK, there is a requirement to be
able to quickly generate recommendations for users on news items as they are
published. However, little has been published about systems that can generate
recommendations in response to changes in recommendable items and user
behaviour in a very short space of time. In this paper we describe a new
algorithm for updating collaborative filtering models incrementally, and
demonstrate its effectiveness on clickstream data from The Times. We also
describe the architecture that allows recommendations to be generated on the
fly, and how we have made each component scalable. The system is currently
being used in production at News UK.Comment: Accepted for presentation at AI-2017 Thirty-seventh SGAI
International Conference on Artificial Intelligence. Cambridge, England 12-14
December 201
Addressing Item-Cold Start Problem in Recommendation Systems using Model Based Approach and Deep Learning
Traditional recommendation systems rely on past usage data in order to
generate new recommendations. Those approaches fail to generate sensible
recommendations for new users and items into the system due to missing
information about their past interactions. In this paper, we propose a solution
for successfully addressing item-cold start problem which uses model-based
approach and recent advances in deep learning. In particular, we use latent
factor model for recommendation, and predict the latent factors from item's
descriptions using convolutional neural network when they cannot be obtained
from usage data. Latent factors obtained by applying matrix factorization to
the available usage data are used as ground truth to train the convolutional
neural network. To create latent factor representations for the new items, the
convolutional neural network uses their textual description. The results from
the experiments reveal that the proposed approach significantly outperforms
several baseline estimators
Modeling social information skills
In a modern economy, the most important resource consists in\ud
human talent: competent, knowledgeable people. Locating the right person for\ud
the task is often a prerequisite to complex problem-solving, and experienced\ud
professionals possess the social skills required to find appropriate human\ud
expertise. These skills can be reproduced more and more with specific\ud
computer software, an approach defining the new field of social information\ud
retrieval. We will analyze the social skills involved and show how to model\ud
them on computer. Current methods will be described, notably information\ud
retrieval techniques and social network theory. A generic architecture and its\ud
functions will be outlined and compared with recent work. We will try in this\ud
way to estimate the perspectives of this recent domain
Multi-modal Embedding Fusion-based Recommender
Recommendation systems have lately been popularized globally, with primary
use cases in online interaction systems, with significant focus on e-commerce
platforms. We have developed a machine learning-based recommendation platform,
which can be easily applied to almost any items and/or actions domain. Contrary
to existing recommendation systems, our platform supports multiple types of
interaction data with multiple modalities of metadata natively. This is
achieved through multi-modal fusion of various data representations. We
deployed the platform into multiple e-commerce stores of different kinds, e.g.
food and beverages, shoes, fashion items, telecom operators. Here, we present
our system, its flexibility and performance. We also show benchmark results on
open datasets, that significantly outperform state-of-the-art prior work.Comment: 7 pages, 8 figure
Situational Enterprise Services
The ability to rapidly find potential business partners as well as rapidly set up a collaborative business process is desirable in the face of market turbulence. Collaborative business processes are increasingly dependent on the integration of business information systems. Traditional linking of business processes has a large ad hoc character. Implementing situational enterprise services in an appropriate way will deliver the business more flexibility, adaptability and agility.
Service-oriented architectures (SOA) are rapidly becoming the dominant computing paradigm. It is now being embraced by organizations everywhere as the key to business agility. Web 2.0 technologies such as AJAX on the other hand provide good user interactions for successful service discovery, selection, adaptation, invocation and service construction. They also balance automatic integration of services and human interactions, disconnecting content from presentation in the delivery of the service. Another Web technology, such as semantic Web, makes automatic service discovery, mediation and composition possible. Integrating SOA, Web 2.0 Technologies and Semantic Web into a service-oriented virtual enterprise connects business processes in a much more horizontal fashion. To be able run these services consistently across the enterprise, an enterprise infrastructure that provides enterprise architecture and security foundation is necessary.
The world is constantly changing. So does the business environment. An agile enterprise needs to be able to quickly and cost-effectively change how it does business and who it does business with. Knowing, adapting to diffident situations is an important aspect of todayâs business environment. The changes in an operating environment can happen implicitly and explicitly. The changes can be caused by different factors in the application domain. Changes can also happen for the purpose of organizing information in a better way. Changes can be further made according to the users' needs such as incorporating additional functionalities. Handling and managing diffident situations of service-oriented enterprises are important aspects of business environment. In the chapter, we will investigate how to apply new Web technologies to develop, deploy and executing enterprise services
Recommendation System for News Reader
Recommendation Systems help users to find information and make decisions where they lack the required knowledge to judge a particular product. Also, the information dataset available can be huge and recommendation systems help in filtering this data according to usersâ needs. Recommendation systems can be used in various different ways to facilitate its users with effective information sorting. For a person who loves reading, this paper presents the research and implementation of a Recommendation System for a NewsReader Application using Android Platform. The NewsReader Application proactively recommends news articles as per the reading habits of the user, recorded over a period of time and also recommends the currently trending articles. Recommendation systems and their implementations using various algorithms is the primary area of study for this project. This research paper compares and details popular recommendation algorithms viz. Content based recommendation systems, Collaborative recommendation systems etc. Moreover, it also presents a more efficient Hybrid approach that absorbs the best aspects from both the algorithms mentioned above, while trying to eliminate all the potential drawbacks observed
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