Online service platforms offering a wide range of services through miniapps have become crucial for users who visit these platforms with clear intentions to find services they are interested in. Aiming at effective content delivery, cross-domain recommendation are introduced to learn high-quality representations by transferring behaviors from data-rich scenarios. However, these methods overlook the impact of the decision path that users take when conduct behaviors, that is, users ultimately exhibit different behaviors based on various intents. To this end, we propose HIER, a novel Hierarchical decIsion path Enhanced Representation learning for cross-domain recommendation. With the help of graph neural networks for high-order topological information of the knowledge graph between multi-source behaviors, we further adaptively learn decision paths through well-designed exemplar-level and information bottleneck based contrastive learning. Extensive experiments in online and offline environments show the superiority of HIER.

Recent AI research has significantly reduced the barriers to apply AI, but the process of setting up the necessary tools and frameworks can still be a challenge. While AI-as-a-Service platforms have emerged to simplify the training and deployment of AI models, they still fall short of achieving true democratization of AI. In this paper, we aim to address this gap by comparing several popular AI-as-a-Service platforms and identifying the key requirements for a platform that can achieve true democratization of AI. Our analysis highlights the need for self-hosting options, high scalability, and openness. To address these requirements, we propose our approach: the "Open Space for Machine Learning" platform. Our platform is built on cutting-edge technologies such as Kubernetes, Kubeflow Pipelines, and Ludwig, enabling us to overcome the challenges of democratizing AI. We argue that our approach is more comprehensive and effective in meeting the requirements of democratizing AI than existing AI-as-a-Service platforms.

Aiming at helping users locally discovery retail services (e.g., entertainment and dinning), Online to Offline (O2O) service platforms have become popular in recent years, which greatly challenge current recommender systems. With the real data in Alipay, a feeds-like scenario for O2O services, we find that recurrence based temporal patterns and position biases commonly exist in our scenarios, which seriously threaten the recommendation effectiveness. To this end, we propose COUPA, an industrial system targeting for characterizing user preference with following two considerations: (1) Time aware preference: we employ the continuous time aware point process equipped with an attention mechanism to fully capture temporal patterns for recommendation. (2) Position aware preference: a position selector component equipped with a position personalization module is elaborately designed to mitigate position bias in a personalized manner. Finally, we carefully implement and deploy COUPA on Alipay with a cooperation of edge, streaming and batch computing, as well as a two-stage online serving mode, to support several popular recommendation scenarios. We conduct extensive experiments to demonstrate that COUPA consistently achieves superior performance and has potential to provide intuitive evidences for recommendation

Day by day the number of machine learning models is increasing at a pace. With this increasing rate, it is hard for beginners to choose an effective model to perform Natural Language Understanding (NLU) and Natural Language Generation (NLG) mechanisms. Researchers across the globe are working around the clock to achieve more progress in artificial intelligence to build agile and intuitive sequence-to-sequence learning models. And in recent times transformers are one such model which gained more prominence in the field of machine learning to perform speech-to-text activities. The wide availability of other sequence-to-sequence learning models like RNNs, LSTMs, and GRU always raises a challenge for beginners when they think about transformers.

Deep convolutional neural network (CNN) based image classification plays an essential role in seamlessly performing most of the challenges from disease diagnosis to predicting consumerism behavior. Using Deep CNN reduces the time and effort required to spend on extracting and selecting classification features manually. In recent times, deep CNN has been applied to image classification … ...

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TTEC Holdings, Inc., a leading digital customer experience technology and services company focused on the design, implementation and delivery of transformative solutions for many of the world's most iconic and disruptive brands, announced a strategic partnership with Pegasystems, Inc., the software company empowering digital transformation at the world's leading enterprises. This partnership will empower clients with industry-leading digital transformation solutions to optimize customer experiences within their contact centers. With the partnership, Pega's world-class intelligent automation and customer engagement suite, combined with TTEC's Customer Experience as a Service platform, will provide the backbone of optimized, digitally driven employee and customer experiences managed by TTEC Digital. The two market leaders will leverage their decades of experience to deliver best-of-breed human and AI-powered intelligence across the customer lifecycle. Together, TTEC and Pega are uniquely positioned to remove the technical and operational obstacles that stand in the way of great experiences for a brand's customers and employees.

A merger between humans and machines is coming, and it's not what you may have thought. Something mysterious flickered into reality when our ancestors first learned to extract knowledge from their heads and embed it in tools. Now, millions of years later, our tools are fusing with us and, in so doing, bringing about something that is part biological and part technological. We are incubating this new intelligence in our organizations, but it is also true that it represents an extension of ourselves. Humanity is like a seed in an enigmatic womb made up of artificial intelligence and automation.

A merger between humans and machines is coming, and it's not what you may have thought. Something mysterious flickered into reality when our ancestors first learned to extract knowledge from their heads and embed it in tools. Now, millions of years later, our tools are fusing with us and, in so doing, bringing about something that is part biological and part technological. We are incubating this new intelligence in our organizations, but it is also true that it represents an extension of ourselves. Humanity is like a seed in an enigmatic womb made up of artificial intelligence and automation.

There is no doubt that machine learning (ML) and artificial intelligence (AI) have the power to accelerate digital transformation, but businesses are just beginning to realise the potential opportunities offered by AI and associated technology. AI is being used to predict outcomes, automate decisions and open up new avenues for generating revenue. Driving efficiencies and digitally transforming organisations of all shapes and sizes. So, if AI isn't part of your digital transformation yet, it should be. Kortical, who use AI to build AI, enabling organisations to build, explain and deploy world class, enterprise grade machine learning and artificial intelligence models, are at the forefront of this trend.