In multi-turn dialogues, large language models (LLM) face a critical challenge of ensuring coherence while adapting to user-specific information. This study introduces the persona knowledge gap, the discrepancy between a model's internal understanding and the knowledge required for coherent, personalized conversations. While prior research has recognized these gaps, computational methods for their identification and resolution remain underexplored. We propose Conversation Preference Elicitation and Recommendation (CPER), a novel framework that dynamically detects and resolves persona knowledge gaps using intrinsic uncertainty quantification and feedback-driven refinement. CPER consists of three key modules: a Contextual Understanding Module for preference extraction, a Dynamic Feedback Module for measuring uncertainty and refining persona alignment, and a Persona-Driven Response Generation module for adapting responses based on accumulated user context. We evaluate CPER on two real-world datasets: CCPE-M for preferential movie recommendations and ESConv for mental health support. Using A/B testing, human evaluators preferred CPER's responses 42% more often than baseline models in CCPE-M and 27% more often in ESConv. A qualitative human evaluation confirms that CPER's responses are preferred for maintaining contextual relevance and coherence, particularly in longer (12+ turn) conversations.

A Florida couple is behind bars for allegedly using an online dating app to lure a 16-year-old girl to their home, brutally torturing and murdering her before dismembering her remains. The body of Miranda Corsette was discarded in a dumpster days after she was reported missing on Feb. 24, according to the St. Petersburg Police Department. Authorities allege that Steven Gress, 35, used the online dating app Grindr to lure Corsette to his house, located approximately 20 miles southwest of Tampa, on Feb. 14. "After meeting him the first time, [Corsette] went home and then the next day she returned to [Gress'] home," police said. Miranda Corsette smiles in an undated photograph shared by the St. Petersburg Police Department. Corsette was allegedly murdered by a man she had met on a dating app, Steven Gress, and his domestic partner, Michelle Brandes.

In recommender systems, the patterns of user behaviors (e.g., purchase, click) may vary greatly in different contexts (e.g., time and location). This is because user behavior is jointly determined by two types of factors: intrinsic factors, which reflect consistent user preference, and extrinsic factors, which reflect external incentives that may vary in different contexts. Differentiating between intrinsic and extrinsic factors helps learn user behaviors better. However, existing studies have only considered differentiating them from a single, pre-defined context (e.g., time or location), ignoring the fact that a user's extrinsic factors may be influenced by the interplay of various contexts at the same time. In this paper, we propose the Intrinsic-Extrinsic Disentangled Recommendation (IEDR) model, a generic framework that differentiates intrinsic from extrinsic factors considering various contexts simultaneously, enabling more accurate differentiation of factors and hence the improvement of recommendation accuracy. IEDR contains a context-invariant contrastive learning component to capture intrinsic factors, and a disentanglement component to extract extrinsic factors under the interplay of various contexts. The two components work together to achieve effective factor learning. Extensive experiments on real-world datasets demonstrate IEDR's effectiveness in learning disentangled factors and significantly improving recommendation accuracy by up to 4% in NDCG.

The writing was already on the wall, but now it's official: The Google Assistant era is over. In a blog post Friday, Google announced plans for Google Assistant's final phase-out, starting on mobile and continuing with tablets, cars, and mobile-connected devices such as headphones and tablets. Finally, Google Assistant will be going away on Nest smart speakers and displays as well as on Google TV devices. Google Assistant's replacement will, of course, be Gemini, Google's entry in the generative AI race. Gemini itself will become the new assistant on Google mobile devices such as phones and tablets, while a "new experience powered by Gemini" is coming to smart speakers and displays.

Google Assistant's days are numbered. Google announced Friday that all Android devices are switching to Gemini as their default assistant and "the classic Google Assistant will no longer be accessible on most mobile devices." The company says it's working to convert more mobile devices from Google Assistant to Gemini in 2025, and plans on "upgrading tablets, cars and devices that connect to your phone, such as headphones and watches" to the new AI assistant. That presumably includes other platforms like iOS, as well. While smart home devices don't seem to be a focus at Google as of late, the company also reaffirmed plans to use Gemini to power a new experience on speakers, displays, and streaming boxes.

Writing about smart home technology, smart devices, and voice assistants is my job. Yet, I don't remember the last time I actually spoke with Alexa. Just to be clear, I don't mean to pick on Alexa per se. I rarely speak to Google Assistant or Apple's Siri, either. It's way easier to haul out my phone and use an app than it is to get a supposedly "smart" voice assistant to do what I want.

In this episode, we dive into Devindra's review of the excellent M4-equipped MacBook Air (and briefly chat about the new Mac Studio). We also discuss Apple's surprise announcement that it's delaying its smarter, AI-infused Siri, which may not arrive until next year. Did Apple over-promise last year, or is it wise to hold off on advanced AI features until they're ready? After all, Apple doesn't want a fiasco like Microsoft's Recall announcement. Listen below or subscribe on your podcast app of choice.

This paper introduces a novel dataset REGEN (Reviews Enhanced with GEnerative Narratives), designed to benchmark the conversational capabilities of recommender Large Language Models (LLMs), addressing the limitations of existing datasets that primarily focus on sequential item prediction. REGEN extends the Amazon Product Reviews dataset by inpainting two key natural language features: (1) user critiques, representing user "steering" queries that lead to the selection of a subsequent item, and (2) narratives, rich textual outputs associated with each recommended item taking into account prior context. The narratives include product endorsements, purchase explanations, and summaries of user preferences. Further, we establish an end-to-end modeling benchmark for the task of conversational recommendation, where models are trained to generate both recommendations and corresponding narratives conditioned on user history (items and critiques). For this joint task, we introduce a modeling framework LUMEN (LLM-based Unified Multi-task Model with Critiques, Recommendations, and Narratives) which uses an LLM as a backbone for critiquing, retrieval and generation. We also evaluate the dataset's quality using standard auto-rating techniques and benchmark it by training both traditional and LLM-based recommender models. Our results demonstrate that incorporating critiques enhances recommendation quality by enabling the recommender to learn language understanding and integrate it with recommendation signals. Furthermore, LLMs trained on our dataset effectively generate both recommendations and contextual narratives, achieving performance comparable to state-of-the-art recommenders and language models.

AirPods are arguably Apple's most popular post-iPhone product, and it sounds like the company has plans to make them even more essential. Bloomberg reports that Apple is adding a live-translate feature to AirPods later this year as part of an upcoming software update. The feature sounds like it would work in a similar way to the translation feature on the Pixel Buds, only without the need to ask Google Assistant or in this case, Siri, to start listening for a specific language first. Apple's feature would reportedly automatically detect that something other than your native language is being spoken, and start converting what you're hearing into a language you understand. Pixel Buds have had live-translation since 2020, one of the few abilities that makes Google's earbuds superior to Apple's.

Microsoft just announced the pending availability of Copilot for Gaming, an AI-powered assistant that's being advertised as a novel way to help players get better at their favorite titles. The company says it will accompany people through games, offering tips, guides and useful information along the way. Microsoft boasts it can also help folks download and launch games, solving the eternal problem of, uh, pressing a button. Copilot for Gaming will be available as part of the Xbox mobile app, so it's being positioned as a second screen type of thing. Copilot for Gaming Soon you'll be able to turn to it for everything from game setup, to tips for finally beating a tough level, wherever you play on Xbox.