For researchers leveraging Large-Language Models (LLMs) in the generation of training datasets, especially for conversational recommender systems - the absence of robust evaluation frameworks has been a long-standing problem Peng et al. (2017). The efficiency brought about by LLMs in the data generation phase is impeded during the process of evaluation of the generated data, since it generally requires human-raters to ensure that the data generated is of high quality and has sufficient diversity. Since the quality of training data is critical for downstream applications, it is important to develop metrics that evaluate the quality holistically and identify biases. In this paper, we present a framework that takes a multi-faceted approach towards evaluating datasets produced by generative models and discuss the advantages and limitations of various evaluation methods.

Self-attentive transformer models have recently been shown to solve the next item recommendation task very efficiently. The learned attention weights capture sequential dynamics in user behavior and generalize well. Motivated by the special structure of learned parameter space, we question if it is possible to mimic it with an alternative and more lightweight approach. We develop a new tensor factorization-based model that ingrains the structural knowledge about sequential data within the learning process. We demonstrate how certain properties of a self-attention network can be reproduced with our approach based on special Hankel matrix representation. The resulting model has a shallow linear architecture and compares competitively to its neural counterpart.

Online personalized recommendation services are generally hosted in the cloud where users query the cloud-based model to receive recommended input such as merchandise of interest or news feed. State-of-the-art recommendation models rely on sparse and dense features to represent users' profile information and the items they interact with. Although sparse features account for 99% of the total model size, there was not enough attention paid to the potential information leakage through sparse features. These sparse features are employed to track users' behavior, e.g., their click history, object interactions, etc., potentially carrying each user's private information. Sparse features are represented as learned embedding vectors that are stored in large tables, and personalized recommendation is performed by using a specific user's sparse feature to index through the tables. Even with recently-proposed methods that hides the computation happening in the cloud, an attacker in the cloud may be able to still track the access patterns to the embedding tables. This paper explores the private information that may be learned by tracking a recommendation model's sparse feature access patterns. We first characterize the types of attacks that can be carried out on sparse features in recommendation models in an untrusted cloud, followed by a demonstration of how each of these attacks leads to extracting users' private information or tracking users by their behavior over time.

Exploiting social relations in recommendation works well because of the effects of social homophily [61] and social influence [60]: (1) social homophily indicates that a user tends to connect herself to other users with similar attributes and preferences, and (2) social influence indicates that users with direct or indirect relations tend to influence each other to make themselves become more similar. Accordingly, SocialRS can effectively mitigate the data sparsity problem by exploiting social neighbors to capture the preferences of a sparsely interacting user. Literature has shown that SocialRS can be applied successfully in various recommendation domains (e.g., product [101, 103], music [116-118], location [39, 72, 100], and image [86, 99, 102]), thereby improving user satisfaction. Furthermore, techniques and insights explored from SocialRS can also be exploited in real-world applications other than recommendations. For instance, García-Sánchez et al. [20] leveraged SocialRS to design a decision-making system for marketing (e.g., advertisement), while Gasparetti et al. [21] analyzed SocialRS in terms of community detection. Motivated by such wide applicability, there has been an increasing interest in research on developing accurate 40 SocialRS models. In the early days, research focused on matrix factorization (MF) techniques [28, 54-20 57, 84, 112].

The anthropologist and famed love expert Helen Fisher seemed ready to dash into oncoming traffic. We were on a sidewalk in Manhattan, opposite the American Museum of Natural History, and nowhere near a safe place to cross the street. She wanted me to stare down the yellow cabs and charge off the curb, though she knew I wouldn't do it: I'd recently taken the personality questionnaire she wrote 17 years ago for a dating website, which produced the insight that I am a cautious, conventional rule follower. She, however, is an "explorer"--she has visited 111 countries, including North Korea--but also, being high in estrogen, a "negotiator" who will use the crosswalk for my benefit. "I am horribly empathetic," she told me. I look into baby carriages and worry about their future with love." This is how Fisher, the 77-year-old chief scientific adviser for Match.com and one of the best-known, most-often-quoted experts on romance and "mate choice," understands life: Personality is a cocktail of ...

The goal of building dialogue agents that can converse with humans naturally has been a long-standing dream of researchers since the early days of artificial intelligence. The well-known Turing Test proposed to judge the ultimate validity of an artificial intelligence agent on the indistinguishability of its dialogues from humans'. It should come as no surprise that human-level dialogue systems are very challenging to build. But, while early effort on rule-based systems found limited success, the emergence of deep learning enabled great advance on this topic. In this thesis, we focus on methods that address the numerous issues that have been imposing the gap between artificial conversational agents and human-level interlocutors. These methods were proposed and experimented with in ways that were inspired by general state-of-the-art AI methodologies. But they also targeted the characteristics that dialogue systems possess.

What exactly is the Fourth Industrial Revolution -- and why should you care? The Fourth Industrial Revolution is a way of describing the blurring of boundaries between the physical, digital, and biological worlds. It's the collective force behind many products and services that are fast becoming indispensable to modern life. Think GPS systems that suggest the fastest route to a destination, voice-activated virtual assistants such as Apple's Siri, personalized Netflix recommendations, and Facebook's ability to recognize your face and tag you in a friend's photo. As a result of this perfect storm of technologies, the Fourth Industrial Revolution is paving the way for transformative changes in the way we live and radically disrupting almost every business sector.

Savvy investors will want to check out stocks that capitalize on artificial intelligence -- using computers to simulate human thinking, reasoning and problem solving. Companies that use AI technology often do so by using software to process huge amounts of data. As the pandemic of 2020 forced lockdowns, many businesses took advantage of AI to interact with the public, as face-to-face contact was limited. That trend seems likely to continue. In a November market forecast, Gartner Inc. predicted the market for AI would grow 21.3%, to $62.5 billion, in 2022.

Let's take your daily life and make it better with AI! You're most likely using apps like Siri or Google Assistant, but do you know the benefits? AIs will assist in tasks that would otherwise require human effort- saving time for everyone. The benefits of using AI are all around you. You just need to take the time to explore them and see what they can do for you! This is an experiment AI tool from Google itself.

I have a love-hate relationship with my alarm. I rely on it to ensure I get up in the morning and perform important tasks throughout the day, but when it sounds in the morning, I glare at it with the fury of a thousand suns. A good alarm clock can make the waking experience less jarring -- with cool features like ramp-up lighting or pleasing nature sounds. But it doesn't stop there; the best alarm clocks have additional features like voice assistants and interactive displays that make your alarm clock more useful than ever. Picking the best alarm clock for you can be tough, especially with so many features, price points and brands.