How to Do It is Slate's sex advice column. Send it to Jessica and Rich here. I am a newly divorced bisexual dad who's moved to a city adjacent to my 20-year-old son's college. He's extremely shy and hasn't talked about sex with me in years. He identifies as queer but has provided no more detail than that.

Augmented Reality assistance are increasingly popular for supporting users with tasks like assembly and cooking. However, current practice typically provide reactive responses initialized from user requests, lacking consideration of rich contextual and user-specific information. To address this limitation, we propose a novel AR assistance system, Satori, that models both user states and environmental contexts to deliver proactive guidance. Our system combines the Belief-Desire-Intention (BDI) model with a state-of-the-art multi-modal large language model (LLM) to infer contextually appropriate guidance. The design is informed by two formative studies involving twelve experts. A sixteen within-subject study find that Satori achieves performance comparable to an designer-created Wizard-of-Oz (WoZ) system without relying on manual configurations or heuristics, thereby enhancing generalizability, reusability and opening up new possibilities for AR assistance.

The contextual multi-armed bandit (MAB) problem is crucial in sequential decision-making. A line of research, known as online clustering of bandits, extends contextual MAB by grouping similar users into clusters, utilizing shared features to improve learning efficiency. However, existing algorithms, which rely on the upper confidence bound (UCB) strategy, struggle to gather adequate statistical information to accurately identify unknown user clusters. As a result, their theoretical analyses require several strong assumptions about the "diversity" of contexts generated by the environment, leading to impractical settings, complicated analyses, and poor practical performance. Removing these assumptions has been a long-standing open problem in the clustering of bandits literature. In this paper, we provide two solutions to this open problem. First, following the i.i.d. context generation setting in existing studies, we propose two novel algorithms, UniCLUB and PhaseUniCLUB, which incorporate enhanced exploration mechanisms to accelerate cluster identification. Remarkably, our algorithms require substantially weaker assumptions while achieving regret bounds comparable to prior work. Second, inspired by the smoothed analysis framework, we propose a more practical setting that eliminates the requirement for i.i.d. context generation used in previous studies, thus enhancing the performance of existing algorithms for online clustering of bandits. Our technique can be applied to both graph-based and set-based clustering of bandits frameworks. Extensive evaluations on both synthetic and real-world datasets demonstrate that our proposed algorithms consistently outperform existing approaches.

The pace of smart home innovation hasn't slowed a whit in 2024, with new products such as the Amazon Echo Show 21 being unveiled just this week. The better news is that the smart home is no longer a niche market appealing only to enthusiasts willing to tolerate steep learning curves. We are, however, still living in a world of smart home silos; namely, Amazon Alexa, Apple HomeKit, and Google Home. The increasingly important Matter standard will unify those ecosystems one day, but that won't happen until Matter incorporates every smart home category. Considering that, we've picked more than one product in several categories, based primarily on which silo your smart home is in.

We experimented with various transformer-based language models, such as BERT [9], RoBERTa [10], and T5 [11] for these datasets to identify the most effective model based on our evaluation. After splitting the restructured datasets into a 90-10 ratio for training and testing, we proceeded to train the selected models (block highlighted in yellow) using a grid search of hyperparameters optimization. Given the multitude of possible tactics available at each proof state, we chose to provide multiple recommendations for the next proof step. To assess the accuracy of these recommendations (block highlighted in green), we use the n-correctness rate, which measures the likelihood that a correct tactic from the testing dataset is among the top-n recommended tactics, where n signifies the number of recommended tactics evaluated against the correct tactic. We found out that RoBERTa demonstrated superior performance across most cases for n = 7.

Let a digital sidekick take the strain. While user fatigue may be setting in – reports suggest a notable decline in usage – the world's biggest online dating company is launching an artificial intelligence assistant that it claims will "transform" online dating. Match Group, the technology company with the world's largest portfolio of dating platforms, has announced it is increasing investment in AI with new products coming in March 2025. An as yet unnamed AI assistant will perform core dating tasks such as selecting the photos it calculates will garner the most responses and recommend what prompts and information to put in a bio. It will also help a user choose the perfect partner.

The cold start problem is a challenging problem faced by most modern recommender systems. By leveraging knowledge from other domains, cross-domain recommendation can be an effective method to alleviate the cold start problem. However, the modelling distortion for long-tail data, which is widely present in recommender systems, is often overlooked in cross-domain recommendation. In this research, we propose a hyperbolic manifold based cross-domain collaborative filtering model using BiTGCF as the base model. We introduce the hyperbolic manifold and construct new propagation layer and transfer layer to address these challenges. The significant performance improvements across various datasets compared to the baseline models demonstrate the effectiveness of our proposed model.

Course selection is a critical aspect of a student's academic journey, significantly impacting their educational experience and future career prospects [Bruch and Feinberg, 2017]. On large campuses such as the University of Michigan, a major public university that offers more than 10,000 courses each year, this process can be quite challenging and time consuming, especially for new students. Traditionally, students have relied on academic advisors and peer networks for guidance in course selection. However, this approach can lead to inequities in access to quality information, as different students may have varying levels of access to knowledgeable peers or experienced advisors [Lynch and O'riordan, 1998]. Traditional recommender systems, such as collaborative filtering, have been employed in various domains to provide personalized recommendations. However, these systems face several limitations when applied to course recommendations in higher education: 1. Lack of interactivity: Traditional systems typically provide static recommendations based on historical data, without the ability to engage in a dynamic dialogue with the user.

Sequential recommendation (SR) systems have evolved significantly over the past decade, transitioning from traditional collaborative filtering to deep learning approaches and, more recently, to large language models (LLMs). While the adoption of LLMs has driven substantial advancements, these models inherently lack collaborative filtering information, relying primarily on textual content data neglecting other modalities and thus failing to achieve optimal recommendation performance. To address this limitation, we propose Molar, a Multimodal large language sequential recommendation framework that integrates multiple content modalities with ID information to capture collaborative signals effectively. Molar employs an MLLM to generate unified item representations from both textual and non-textual data, facilitating comprehensive multimodal modeling and enriching item embeddings. Additionally, it incorporates collaborative filtering signals through a post-alignment mechanism, which aligns user representations from content-based and ID-based models, ensuring precise personalization and robust performance. By seamlessly combining multimodal content with collaborative filtering insights, Molar captures both user interests and contextual semantics, leading to superior recommendation accuracy. Extensive experiments validate that Molar significantly outperforms traditional and LLM-based baselines, highlighting its strength in utilizing multimodal data and collaborative signals for sequential recommendation tasks. The source code is available at https://anonymous.4open.science/r/Molar-8B06/.

Matrix completion has gained considerable interest in recent years. The goal of matrix completion is to predict the unknown entries of a partially observed matrix using its known entries. Although common applications feature discrete rating-scale data, such as user-product rating matrices in recommender systems or surveys in the social and behavioral sciences, methods for matrix completion are almost always designed for and studied in the context of continuous data. Furthermore, only a small subset of the literature considers matrix completion in the presence of corrupted observations despite their common occurrence in practice. Examples include attacks on recommender systems (i.e., malicious users deliberately manipulating ratings to influence the recommender system to their advantage), or careless respondents in surveys (i.e., respondents providing answers irrespective of what the survey asks of them due to a lack of attention). We introduce a matrix completion algorithm that is tailored towards the discrete nature of rating-scale data and robust to the presence of corrupted observations. In addition, we investigate the performance of the proposed method and its competitors with discrete rating-scale (rather than continuous) data as well as under various missing data mechanisms and types of corrupted observations.