In this paper, we propose a generic model-based re-ranking framework, MultiSlot ReRanker, which simultaneously optimizes relevance, diversity, and freshness. Specifically, our Sequential Greedy Algorithm (SGA) is efficient enough (linear time complexity) for large-scale production recommendation engines. It achieved a lift of $+6\%$ to $ +10\%$ offline Area Under the receiver operating characteristic Curve (AUC) which is mainly due to explicitly modeling mutual influences among items of a list, and leveraging the second pass ranking scores of multiple objectives. In addition, we have generalized the offline replay theory to multi-slot re-ranking scenarios, with trade-offs among multiple objectives. The offline replay results can be further improved by Pareto Optimality. Moreover, we've built a multi-slot re-ranking simulator based on OpenAI Gym integrated with the Ray framework. It can be easily configured for different assumptions to quickly benchmark both reinforcement learning and supervised learning algorithms.

In this paper, we explore how techniques from soft robotics can help create a new form of robot expression. We present Sprout, a soft expressive robot that conveys its internal states by changing its body shape. Sprout can extend, bend, twist, and expand using fiber-embedded actuators integrated into its construction. These deformations enable Sprout to express its internal states, for example, by expanding to express anger and bending its body sideways to express curiosity. Through two user studies, we investigated how users interpreted Sprout's expressions, their perceptions of Sprout, and their expectations from future iterations of Sprout's design. We argue that the use of soft actuators opens a novel design space for robot expressions to convey internal states, emotions, and intent.

Yes, you probably already have a virtual assistant in your pocket on your phone. Heck, if you're reading Engadget, I'm willing to bet you've got at least one smart speaker floating around your home as well that you can ask to complete basic tasks. But a new start up called Rabbit seems to think these are less than ideal implementations of AI (if you can really call Siri and Alexa that). It envisions a world where you trade apps for conversation and, rather than a distracting device shoving icons in your face, you interact with what amounts to a walkie-talkie for an AI. The R1 is the first device to be launched by Rabbit and it's an objectively adorable little square in an endearingly bright shade of orange.

This paper aims to develop a semi-formal design space for Human-AI interactions, by building a set of interaction primitives which specify the communication between users and AI systems during their interaction. We show how these primitives can be combined into a set of interaction patterns which can provide an abstract specification for exchanging messages between humans and AI/ML models to carry out purposeful interactions. The motivation behind this is twofold: firstly, to provide a compact generalisation of existing practices, that highlights the similarities and differences between systems in terms of their interaction behaviours; and secondly, to support the creation of new systems, in particular by opening the space of possibilities for interactions with models. We present a short literature review on frameworks, guidelines and taxonomies related to the design and implementation of HAI interactions, including human-in-the-loop, explainable AI, as well as hybrid intelligence and collaborative learning approaches. From the literature review, we define a vocabulary for describing information exchanges in terms of providing and requesting particular model-specific data types. Based on this vocabulary, a message passing model for interactions between humans and models is presented, which we demonstrate can account for existing systems and approaches. Finally, we build this into design patterns as mid-level constructs that capture common interactional structures. We discuss how this approach can be used towards a design space for Human-AI interactions that creates new possibilities for designs as well as keeping track of implementation issues and concerns.

At least, that was my takeaway after my first chat with the founder of Rabbit Inc., a new AI startup debuting a pocket-friendly device called the R1 at CES 2024. Instead of taking out your smartphone to complete some task, hunting for the right app, and then tapping around inside it, Lyu wants us to just ask the R1 via a push-to-talk button. Then a series of automated scripts called "rabbits" will carry out the task so you can go about your day. The R1 is a red-orange, square-ish device about the size of a stack of Post-It notes. It was designed in collaboration with the Swedish firm Teenage Engineering.

Google is expanding its smart home integration at CES 2024. The company said Tuesday that, in the future, LG TVs and some Google TV (and other Android TV) products will work as Google Home hubs. Considering Google's support for the Matter smart home standard, the move could make it easier for customers to set up and control their smart homes without buying a Nest device. "In the future, LG TVs and select Google TV and other Android TV OS devices will act as hubs for Google Home," Google Android VP Sameer Samat wrote in today's announcement blog post. "So if you have a Nest Hub, Nest Mini or compatible TV, it's easy to add Matter devices to your home network and locally control them with the Google Home app."

How to Do It is Slate's sex advice column. Send it to Stoya and Rich here. As a sexually dominant-leaning female, I get a lot of instant gratification out of gorgeous women online telling me my assertiveness is impressive and sexy. When I have sex with women in my dreams, it's perfect. While my "traditional" long-term relationships have been with male-presenting people, I slept with several women in my early 20s--though I struggled to find satisfying connections.

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Modeling long user histories plays a pivotal role in enhancing recommendation systems, allowing to capture users' evolving preferences, resulting in more precise and personalized recommendations. In this study, we tackle the challenges of modeling long user histories for preference understanding in natural language. Specifically, we introduce a new User Embedding Module (UEM) that efficiently processes user history in free-form text by compressing and representing them as embeddings, to use them as soft prompts to a LM. Our experiments demonstrate the superior capability of this approach in handling significantly longer histories compared to conventional text-based methods, yielding substantial improvements in predictive performance. Models trained using our approach exhibit substantial enhancements, with up to 0.21 and 0.25 F1 points improvement over the text-based prompting baselines. The main contribution of this research is to demonstrate the ability to bias language models via user signals.

In today's data-rich environment, recommender systems play a crucial role in decision support systems. They provide to users personalized recommendations and explanations about these recommendations. Embedding-based models, despite their widespread use, often suffer from a lack of interpretability, which can undermine trust and user engagement. This paper presents an approach that combines embedding-based and semantic-based models to generate post-hoc explanations in recommender systems, leveraging ontology-based knowledge graphs to improve interpretability and explainability. By organizing data within a structured framework, ontologies enable the modeling of intricate relationships between entities, which is essential for generating explanations. By combining embedding-based and semantic based models for post-hoc explanations in recommender systems, the framework we defined aims at producing meaningful and easy-to-understand explanations, enhancing user trust and satisfaction, and potentially promoting the adoption of recommender systems across the e-commerce sector.