Machine Learning and Data Science Consulting firm focused on increasing revenue for clients. Leave competitors in your ecommerce niche gasping for air with these machine learning tools that automate costs out and show you where your customers are hiding. Ecommerce is one of the fastest growing industries in the world, totaling 4.88 trillion dollars in 2019, and an expected gain of 20% each year. With growth this fast and an industry that reinvents itself all the time it's important to keep up with the latest technologies that make your business successful. Companies are using machine learning and deep learning to make all sorts of strides in ecommerce sites that are unimaginable to people unfamiliar.

Conversational systems are of primary interest in the AI community. Chatbots are increasingly being deployed to provide round-the-clock support and to increase customer engagement. Many of the commercial bot building frameworks follow a standard approach that requires one to build and train an intent model to recognize a user input. Intent models are trained in a supervised setting with a collection of textual utterance and intent label pairs. Gathering a substantial and wide coverage of training data for different intent is a bottleneck in the bot building process. Moreover, the cost of labeling a hundred to thousands of conversations with intent is a time consuming and laborious job. In this paper, we present an intent discovery framework that involves 4 primary steps: Extraction of textual utterances from a conversation using a pre-trained domain agnostic Dialog Act Classifier (Data Extraction), automatic clustering of similar user utterances (Clustering), manual annotation of clusters with an intent label (Labeling) and propagation of intent labels to the utterances from the previous step, which are not mapped to any cluster (Label Propagation); to generate intent training data from raw conversations. We have introduced a novel density-based clustering algorithm ITER-DBSCAN for unbalanced data clustering. Subject Matter Expert (Annotators with domain expertise) manually looks into the clustered user utterances and provides an intent label for discovery. We conducted user studies to validate the effectiveness of the trained intent model generated in terms of coverage of intents, accuracy and time saving concerning manual annotation. Although the system is developed for building an intent model for the conversational system, this framework can also be used for a short text clustering or as a labeling framework.

Hussein Fazal SnapTravel Toronto, Canada hussein@snaptravel.com Abstract --In this paper, we present a real-world conversational AI system to search for and book hotels through text messaging. Our architecture consists of a frame-based dialogue management system, which calls machine learning models for intent classification, named entity recognition, and information retrieval subtasks. Our chatbot has been deployed on a commercial scale, handling tens of thousands of hotel searches every day. We describe the various opportunities and challenges of developing a chatbot in the travel industry. Index T erms--conversational AI, task-oriented chatbot, named entity recognition, information retrieval I. I NTRODUCTION Task-oriented chatbots have recently been applied to many areas in e-commerce.

Cisco has unveiled what it's calling "intent-based networking solutions" which, it claims, are the culmination of its vision to create "an intuitive system that anticipates actions, stops security threats in their tracks, and continues to evolve and learn. It will help businesses to unlock new opportunities and solve previously unsolvable challenges in an era of increasing connectivity and distributed technology." Cisco's pitch is that companies are currently managing their networks through traditional IT processes that are not now sustainable as the network grows and gets more complex - there are just too many buttons to push and adjustments to be made, so the whole construction is becoming unmanageable, it claims. So in response Cisco's says it's harnessed the concept of'intent networking' to create an "intuitive system that uses AI and machine learning to constantly learn, adapt, automate and protect, to optimize network operations and defend against today's evolving threat landscape." The broad idea of an'intent model' is easy enough to understand and one of its most robust originators and promoters has been Tom Nolle of CIMI Research who, as I thought he might, has popped up today with a timely blog post on the Cisco launch.

With rapid growth of commercial search engines, detecting multilingual and multi-regional intent underlying search queries becomes a critical challenge to serve international users with diverse language and region requirements. We introduce a query intent probabilistic model, whose input is the number of clicks on documents from different regions and in different language, while the output of this model is a smoothed probabilistic distribution of multilingual and multi-regional query intent. Based on an editorial test to evaluate the accuracy of the intent classifier, our probabilistic model could improve the accuracy of multilingual intent detection for 15%, and improve multi-regional intent detection for 18%. To improve web search quality, we propose a set of new ranking features to combine multilingual and multi-regional query intent with document language/region attributes, and apply different approaches in integrating intent information to directly affect ranking. The experiments show that the novel features could provide 2.31% NDCG@1 improvement and 1.81% NDCG@5 improvement.