Thank you for calling …" A sad sort of human-to-computer stalemate has played out over countless fruitless interactions. Companies adopted IVR (interactive voice response) systems over the last decades, possibly in an attempt to reduce the cost of hiring and training costs for human customer service reps that have high turnover rates. Or, perhaps some forward-thinking executives thought a robot-voiced CSR would make a company appear more'advanced' in comparison to its competitors. Whatever the reason, our earliest conversations with IVR menus and chatbots left most of us humans feeling let down, like we weren't having a conversation at all. Despite the fact that voice recognition and computer speech have improved dramatically in speed and sophistication, it's hard for some of us to shake that feeling that nobody is on the other end of the line to help.

Recommender systems, which offer personalized suggestions to users, power many of today's social media, e-commerce and entertainment. However, these systems have been known to intellectually isolate users from a variety of perspectives, or cause filter bubbles. In our work, we characterize and mitigate this filter bubble effect. We do so by classifying various datapoints based on their user-item interaction history and calculating the influences of the classified categories on each other using the well known TracIn method. Finally, we mitigate this filter bubble effect without compromising accuracy by carefully retraining our recommender system.

Collaborative Filtering (CF) is a widely used technique which allows to leverage past users' preferences data to identify behavioural patterns and exploit them to predict custom recommendations. In this work, we illustrate our review of different CF techniques in the context of the Computational Intelligence Lab (CIL) CF project at ETH Z\"urich. After evaluating the performances of the individual models, we show that blending factorization-based and similarity-based approaches can lead to a significant error decrease (-9.4%) on the best-performing stand-alone model. Moreover, we propose a novel stochastic extension of a similarity model, SCSR, which consistently reduce the asymptotic complexity of the original algorithm.

Since the development of writing 5000 years ago, human-generated data gets produced at an ever-increasing pace. Classical archival methods aimed at easing information retrieval. Nowadays, archiving is not enough anymore. The amount of data that gets generated daily is beyond human comprehension, and appeals for new information retrieval strategies. Instead of referencing every single data piece as in traditional archival techniques, a more relevant approach consists in understanding the overall ideas conveyed in data flows. To spot such general tendencies, a precise comprehension of the underlying data generation mechanisms is required. In the rich literature tackling this problem, the question of information interaction remains nearly unexplored. First, we investigate the frequency of such interactions. Building on recent advances made in Stochastic Block Modelling, we explore the role of interactions in several social networks. We find that interactions are rare in these datasets. Then, we wonder how interactions evolve over time. Earlier data pieces should not have an everlasting influence on ulterior data generation mechanisms. We model this using dynamic network inference advances. We conclude that interactions are brief. Finally, we design a framework that jointly models rare and brief interactions based on Dirichlet-Hawkes Processes. We argue that this new class of models fits brief and sparse interaction modelling. We conduct a large-scale application on Reddit and find that interactions play a minor role in this dataset. From a broader perspective, our work results in a collection of highly flexible models and in a rethinking of core concepts of machine learning. Consequently, we open a range of novel perspectives both in terms of real-world applications and in terms of technical contributions to machine learning.

The emerging meta- and multi-verse landscape is yet another step towards the more prevalent use of already ubiquitous online markets. In such markets, recommender systems play critical roles by offering items of interest to the users, thereby narrowing down a vast search space that comprises hundreds of thousands of products. Recommender systems are usually designed to learn common user behaviors and rely on them for inference. This approach, while effective, is oblivious to subtle idiosyncrasies that differentiate humans from each other. Focusing on this observation, we propose an architecture that relies on common patterns as well as individual behaviors to tailor its recommendations for each person. Simulations under a controlled environment show that our proposed model learns interpretable personalized user behaviors. Our empirical results on Nielsen Consumer Panel dataset indicate that the proposed approach achieves up to 27.9% performance improvement compared to the state-of-the-art.

It's a situation familiar to anyone who's ever communicated with a voice assistant on a smart device. You pose a request: "Hey Voice Assistant, tell me a story about Georgia Tech." More often than not, you get a related response – "Georgia Tech is located in Atlanta, Georgia. Would you like me to provide you with directions?" – but one with slightly unnatural language and only limited information. Despite the enormous strides made in artificial intelligence to develop systems that can answer simple questions and requests, the kinds of natural conversational language humans have with each other when giving more complex directions or telling stories has thus far been out of reach.

Financial services and technology vendors make for uneasy bedfellows. While tech has formed banking's bedrock since the Big Bang deregulation of the 1980s, in the last decade financial services (FS) organisations have seen the new "masters of the universe" steadily – almost stealthily – encroach on their patch. Established tech vendors and new start-ups have introduced a range of financial services from money transfer apps to mobile payments, crowdfunding to share trading and investments. These new services are perfectly suited to a generation who have grown up with smartphones and expect instant access to digital services, combined simplicity and a great user experience. While over the last few years there has been an exponential increase in the structured data that is collected and used, the inclusion of unstructured data sets, pictures, images and videos along with structured data has been increasingly important in driving both strategic and operational business decisions.

An effective content recommendation on social media platforms should be able to benefit both creators to earn fair compensation and consumers to enjoy really relevant, interesting, and personalized content. In this paper, we propose a model to implement the liquid democracy principle for the content recommendation system. It uses a personalized recommendation model based on reputation ranking system to encourage personal interests driven recommendation. Moreover, the personalization factors to an end users' higher-order friends on the social network (initial input Twitter channels in our case study) to improve the accuracy and diversity of recommendation results. This paper analyzes the dataset based on cryptocurrency news on Twitter to find the opinion leader using the liquid rank reputation system. This paper deals with the tier-2 implementation of a liquid rank in a content recommendation model. This model can be also used as an additional layer in the other recommendation systems. The paper proposes the implementation, challenges, and future scope of the liquid rank reputation model.

Due to its deep learning and independent decision-making capabilities, applications of AI in different business areas are seeing a steady rise in ubiquity in some industries. The concept of artificial intelligence or machines that aim to emulate human thinking is undergoing vigorous research and is a topic that is increasingly being associated with the Internet of things. An AI enabled IoT system extends the functionality and value of an organization's offering, without the need for committing additional resources to achieve the increased value. This is exemplified by under Armour(UA) and IBM's collaboration on the UA Record app, which is an AI-based personal fitness coaching system, that uses a variety of sensor data to suggest highly personalized, context-relevant fitness activities to users. Such applications of AI are going to be more commonplace in the future as they are already having a significant impact on many industries.

Amazon wants Alexa owners to buy more things. That's the clear impetus behind the new Alexa feature announced today at Amazon's Accelerate conference, called Customers Ask Alexa, which lets brands submit answers to common questions like "How can I remove pet hair from my carpet?" and "How to eliminate odor from soil stains?" Previously, Alexa supplied generic tips from the web and other sources in response to such queries. But Customers Ask Alexa basically turns answers into sponsored product spots. "Brands registered with Amazon Brand Registry will see the new Customers Ask Alexa feature in Seller Central, where they can easily discover and answer frequently asked customer questions using self-service tools," Amazon explains in a blog post.