Conversational agents have been gaining increasing popularity in recent years. Influenced by the widespread adoption of task-oriented agents such as Apple Siri and Amazon Alexa, these agents are being deployed into various applications to enhance user experience. Although these agents promote "ask me anything" functionality, they are typically built to focus on a single or finite set of expertise. Given that complex tasks often require more than one expertise, this results in the users needing to learn and adopt multiple agents. One approach to alleviate this is to abstract the orchestration of agents in the background. However, this removes the option of choice and flexibility, potentially harming the ability to complete tasks. In this paper, we explore these different interaction experiences (one agent for all) vs (user choice of agents) for conversational AI. We design prototypes for each, systematically evaluating their ability to facilitate task completion. Through a series of conducted user studies, we show that users have a significant preference for abstracting agent orchestration in both system usability and system performance. Additionally, we demonstrate that this mode of interaction is able to provide quality responses that are rated within 1% of human-selected answers.

A new smart Smart toilets showcased at CES give'porcelain throne' a new meaning. Attendees of the Las Vegas event feasted their eyes on innovative loos designed by appliance maker Kholer, which feature voice activation, touch screens and high prices. For 4,500, the Veil includes a range of services like pulsating spray options and automatic deodorizer, while Amazon's Alexa powers the top-of-the-line Numi, which costs around 10,000. The company did display a more affordable option - the 1,200 PureWash E930 bidet that connects to your home internet and features voice control. The Kohler Veil, released this year at CES, is Kohler's mid-range smart toilet.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. An Indiana woman has been sentenced to three years in federal prison after she used a dating app to scam a 96-year-old man out of nearly 80,000, a U.S. attorney announced Wednesday. Brittany Rakia Shawnai Lasley, 34, of Anderson, created a social media account containing fake profile information on the dating site "Plenty of Fish" and used the account to perpetrate an online romance with the man, who was a windower, according to U.S. Attorney Zachary Cunha. Over time, Lasley persuaded the 96-year-old to send her money, gift cards, credit cards and even to hand over sensitive banking information.

Recommendation systems are ubiquitous, from Spotify playlist suggestions to Amazon product suggestions. Nevertheless, depending on the methodology or the dataset, these systems typically fail to capture user preferences and generate general recommendations. Recent advancements in Large Language Models (LLM) offer promising results for analyzing user queries. However, employing these models to capture user preferences and efficiency remains an open question. In this paper, we propose LLMRS, an LLM-based zero-shot recommender system where we employ pre-trained LLM to encode user reviews into a review score and generate user-tailored recommendations. We experimented with LLMRS on a real-world dataset, the Amazon product reviews, for software purchase use cases. The results show that LLMRS outperforms the ranking-based baseline model while successfully capturing meaningful information from product reviews, thereby providing more reliable recommendations.

The rapid expansion of online information from diverse sources and the growing multilingual nature of the web underscore the escalating significance of information retrieval (IR) and recommender systems (RS). Today's web is no longer limited to a single language, but is increasingly rich in multiple languages, mirroring the multilingual capacities of its global users Steichen et al. [2014], Tashu et al. [2023]. This diversity highlights the urgent need for cross-lingual recommender systems. Traditional recommender systems often prioritize content in a single language, sidelining a wealth of multilingual documents that may hold valuable insights. This gap leads to the emergence of cross-language information access, where recommender systems suggest items in different languages based on user queries Lops et al. [2010], Narducci et al. [2016], Salamon et al. [2021]. Machine Learning and Deep Learning, which have significantly impacted language representation and processing, are pivotal to enhancing information retrieval and recommender systems, especially in the realm of document recom-The result presented in this work is based on Eduard-Raul Kontos's bachelor project while he was at the University of Groningen

In this work, we have proposed an approach for improving the GCN for predicting ratings in social networks. Our model is expanded from the standard model with several layers of transformer architecture. The main focus of the paper is on the encoder architecture for node embedding in the network. Using the embedding layer from the graph-based convolution layer, the attention mechanism could rearrange the feature space to get a more efficient embedding for the downstream task. The experiments showed that our proposed architecture achieves better performance than GCN on the traditional link prediction task.

The effectiveness of recommendation systems is pivotal to user engagement and satisfaction in online platforms. As these recommendation systems increasingly influence user choices, their evaluation transcends mere technical performance and becomes central to business success. This paper addresses the multifaceted nature of recommendations system evaluation by introducing a comprehensive suite of metrics, each tailored to capture a distinct aspect of system performance. We discuss * Similarity Metrics: to quantify the precision of content-based filtering mechanisms and assess the accuracy of collaborative filtering techniques. * Candidate Generation Metrics: to evaluate how effectively the system identifies a broad yet relevant range of items. * Predictive Metrics: to assess the accuracy of forecasted user preferences. * Ranking Metrics: to evaluate the effectiveness of the order in which recommendations are presented. * Business Metrics: to align the performance of the recommendation system with economic objectives. Our approach emphasizes the contextual application of these metrics and their interdependencies. In this paper, we identify the strengths and limitations of current evaluation practices and highlight the nuanced trade-offs that emerge when optimizing recommendation systems across different metrics. The paper concludes by proposing a framework for selecting and interpreting these metrics to not only improve system performance but also to advance business goals. This work is to aid researchers and practitioners in critically assessing recommendation systems and fosters the development of more nuanced, effective, and economically viable personalization strategies. Our code is available at GitHub - https://github.com/aryan-jadon/Evaluation-Metrics-for-Recommendation-Systems.

Samsung showed off a remodeled Ballie, a sunshine-yellow autonomously driving robot, at CES 2024. Described as an "at-home assistant," this bowling ball of a robot is designed to answer your phone calls, play calming music, display the hottest news stories, and more. Maybe I'm the type of person that's easily charmed by whimsical things, but this little dude knocked the contrarian right out of me. One of the cooler things about Ballie is its built-in 1080p projector and spatial LiDAR sensor. That means it'll project movies and conference calls on the floor, wall, or any other hard surface.

Rep. Cory Mills, R-Fla., sits down with'FOX & Friends Weekend' to discuss Ukraine funding, Biden's border policies and attacks on U.S. bases in the Middle East. The U.S. Embassy in Bogota, Colombia, is warning Americans traveling to the country not to use dating apps after eight "suspicious deaths" of private U.S. citizens. According to the embassy, the deaths -- potentially involuntary drug overdoes or suspected homicides -- took place in Medellin between November 1 and December 31, 2023. "Over the last year, the Embassy has seen an increase in reports of incidents involving the use of online dating applications to lure victims, typically foreigners, for robbery by force or using sedatives to drug and rob individuals," the embassy said. The Embassy said it regularly receives reports of such incidents occurring in major cities, like Medellin, Cartagena, and Bogota.

Harvard University offers a behemoth of courses that teach its students topics including "Queering Education," "Black Radicalism" and sexual fetishes. However, its course catalog – while offering many topics some would consider strongly critical of America – shows it does not offer significant courses focusing on American patriotism in depth despite taking in hundreds of millions of taxpayer dollars every year. In 2021, Harvard received 625 million from American taxpayers, all the while the Ivy League boasts over 50 billion in its endowment. Some companies and prospective students are starting to question their interest in Harvard, particularly after scandals relating to alleged pervasive antisemitism and pro-Hamas sentiment on its campus – prompting legal action and a civil rights investigation from the U.S. Department of Education. Harvard's education department for prospective K-12 teachers elaborates on how one can bring queerness and transgenderism into schools.