AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders

Collaborative filtering (CF) has been successfully used to provide users with personalized products and services. However, dealing with the increasing sparseness of user-item matrix still remains a challenge. To tackle such issue, hybrid CF such as combining with content based filtering and leveraging side information of users and items has been extensively studied to enhance performance. However, most of these approaches depend on hand-crafted feature engineering, which are usually noise-prone and biased by different feature extraction and selection schemes. In this paper, we propose a new hybrid model by generalizing contractive auto-encoder paradigm into matrix factorization framework with good scalability and computational efficiency, which jointly model content information as representations of effectiveness and compactness, and leverage implicit user feedback to make accurate recommendations. Extensive experiments conducted over three large scale real datasets indicate the proposed approach outperforms the compared methods for item recommendation.Comment: 4 pages, 3 figure

A resource-oriented architecture for business process systems

Background: The REpresentational State Transfer (REST) design principles treat all concepts in the world as link-connected resources, and support ROA (Resource-Oriented Architecture) for the Web applications. REST and ROA are responsible for the adaptability achieved in the Web. Some design approaches of Web-based business process systems recently evolved towards RESTful to inherit adaptability. However, none of the approaches can improve the adaptability of the produced systems. Aims: Propose a systematic approach for design and execution of Web-based business processes to improve adaptability of the produced systems. Methods: This research followed an empirical research methodology, which evaluates research solutions with real-world cases. On one hand, the research solution was derived by 1) tailoring the REST principles towards business process systems; 2) proposing REST annotations on existing business process modelling; 3) mapping the concepts of business process to HTTP/URI specifications; and 4) designing a format for process context information. On the other hand, the research solution was evaluated through three real-world case studies. Two of the case studies conducted comparative analysis in terms of adaptability of the systems produced by the proposed approach and two alternatives, namely, SOA and MEST (MESsage Transfer). The analysis is based on metrics, including LOC difference, change locality, coupling and cohesion, and an analysis framework called BASE. Results: The research solution is ROA4BP, which includes 1) an architecting approach for design and implementation of Web-based business processes to provide a development guideline; 2) a set of REST-related annotations on existing process modelling to ensure the compatibility with existing techniques; 3) A systematic mapping between business process and HTTP/URI specifications to utilize the advanced mechanisms provided by the Web infrastructure; and 4) a communication format to exchange structured process context information during runtime among process participants. A modelling tool, a programming API and a runtime engine were implemented to support the approach and simplify the implementation of case studies. The case studies demonstrated that ROA4BP can produce more adaptable business process systems compared to the other two alternatives. Conclusion: ROA4BP can help to design and execute RESTful business process systems with better adaptability at design-time and runtime

Responsible-AI-by-Design: a Pattern Collection for Designing Responsible AI Systems

Although AI has significant potential to transform society, there are serious concerns about its ability to behave and make decisions responsibly. Many ethical regulations, principles, and guidelines for responsible AI have been issued recently. However, these principles are high-level and difficult to put into practice. In the meantime much effort has been put into responsible AI from the algorithm perspective, but they are limited to a small subset of ethical principles amenable to mathematical analysis. Responsible AI issues go beyond data and algorithms and are often at the system-level crosscutting many system components and the entire software engineering lifecycle. Based on the result of a systematic literature review, this paper identifies one missing element as the system-level guidance - how to design the architecture of responsible AI systems. We present a summary of design patterns that can be embedded into the AI systems as product features to contribute to responsible-AI-by-design