"I'm not going to respond to that," Siri responded. I had just cursed at it, and this was my passive-aggressive chastisement. The cursing was, in my view, warranted. I was in my car, running errands, and had found myself in an unfamiliar part of town. I requested "directions to Lowe's," hoping to get routed to the big-box hardware store without taking my eyes off the road.

The emergence of conversational assistants has fundamentally reshaped user interactions with digital platforms. This paper introduces Flippi-a cutting-edge, end-to-end conversational assistant powered by large language models (LLMs) and tailored for the e-commerce sector. Flippi addresses the challenges posed by the vast and often overwhelming product landscape, enabling customers to discover products more efficiently through natural language dialogue. By accommodating both objective and subjective user requirements, Flippi delivers a personalized shopping experience that surpasses traditional search methods. This paper details how Flippi interprets customer queries to provide precise product information, leveraging advanced NLP techniques such as Query Reformulation, Intent Detection, Retrieval-Augmented Generation (RAG), Named Entity Recognition (NER), and Context Reduction. Flippi's unique capability to identify and present the most attractive offers on an e-commerce site is also explored, demonstrating how it empowers users to make cost-effective decisions. Additionally, the paper discusses Flippi's comparative analysis features, which help users make informed choices by contrasting product features, prices, and other relevant attributes. The system's robust architecture is outlined, emphasizing its adaptability for integration across various e-commerce platforms and the technological choices underpinning its performance and accuracy. Finally, a comprehensive evaluation framework is presented, covering performance metrics, user satisfaction, and the impact on customer engagement and conversion rates. By bridging the convenience of online shopping with the personalized assistance traditionally found in physical stores, Flippi sets a new standard for customer satisfaction and engagement in the digital marketplace.

The sale event of the summer is here, and the Amazon Prime Day Kindle deals (and other Amazon device deals!) are ones you can count on being great to shop. As someone who tests Amazon gear for a living--Echo speakers, Kindles, Amazon's smart plugs, you name it--I'm always tracking what in Amazon's lineup is worth buying. I have a ton of favorites, from the best of the best, like the Kindle Paperwhite and Echo Show 8, to the Kindle Scribe and Echo Spot. It's the perfect time to shop all of those and more. Not only is everything in this guide WIRED-tested and WIRED-approved, but it's on sale right now.

Amazon Prime Day is four days in 2025, and we've reached the final day. The Prime Day deals started dropping last month and end at midnight tonight (Friday, July 11). We have been working in shifts, covering 20 hours a day through the end, in a dangerously caffeinated state--all to help you nab the best Prime Day deals with up-to-date recommendations. The WIRED Reviews team only recommends deals on products we've tested and approved, and which are actually discounted. If you're looking for up-to-the-minute coverage of deals, check out our Amazon Prime Day liveblog, which will run from 5 am to midnight daily. If you're coming to Prime Day looking for something dirt-cheap, I've got one for you. Yes, this device is a Chromebook, but as a "Chromebook Plus" model, it's a big step up from the reputation these laptops have when kids are introduced to them in schools. The Acer Chromebook Plus 515 comes with a 1080p display, a spacious 15.6-inch display, and an Intel Core i3 processor.

Recommender systems powered by generative models (Gen-RecSys) extend beyond classical item ranking by producing open-ended content, which simultaneously unlocks richer user experiences and introduces new risks. On one hand, these systems can enhance personalization and appeal through dynamic explanations and multi-turn dialogues. On the other hand, they might venture into unknown territory-hallucinating nonexistent items, amplifying bias, or leaking private information. Traditional accuracy metrics cannot fully capture these challenges, as they fail to measure factual correctness, content safety, or alignment with user intent. This paper makes two main contributions. First, we categorize the evaluation challenges of Gen-RecSys into two groups: (i) existing concerns that are exacerbated by generative outputs (e.g., bias, privacy) and (ii) entirely new risks (e.g., item hallucinations, contradictory explanations). Second, we propose a holistic evaluation approach that includes scenario-based assessments and multi-metric checks-incorporating relevance, factual grounding, bias detection, and policy compliance. Our goal is to provide a guiding framework so researchers and practitioners can thoroughly assess Gen-RecSys, ensuring effective personalization and responsible deployment.

Explanations play a variety of roles in various recommender systems, from a legally mandated afterthought, through an integral element of user experience, to a key to persuasiveness. A natural and useful form of an explanation is the Counterfactual Explanation (CE). We present a method for generating highly plausible CEs in recommender systems and evaluate it both numerically and with a user study.

Traditional recommendation algorithms are not designed to provide personalized recommendations based on user preferences provided through text, e.g., "I enjoy light-hearted comedies with a lot of humor". Large Language Models (LLMs) have emerged as one of the most promising tools for natural language processing in recent years. This research proposes a novel framework that mimics how a close friend would recommend items based on their knowledge of an individual's tastes. We leverage LLMs to enhance movie recommendation systems by refining traditional algorithm outputs and integrating them with language-based user preference inputs. We employ Singular Value Decomposition (SVD) or SVD++ algorithms to generate initial movie recommendations, implemented using the Surprise Python library and trained on the MovieLens-Latest-Small dataset. We compare the performance of the base algorithms with our LLM-enhanced versions using leave-one-out validation hit rates and cumulative hit rates. Additionally, to compare the performance of our framework against the current state-of-the-art recommendation systems, we use rating and ranking metrics with an item-based stratified 0.75 train, 0.25 test split. Our framework can generate preference profiles automatically based on users' favorite movies or allow manual preference specification for more personalized results. Using an automated approach, our framework overwhelmingly surpassed SVD and SVD++ on every evaluation metric used (e.g., improvements of up to ~6x in cumulative hit rate, ~3.7x in NDCG, etc.), albeit at the cost of a slight increase in computational overhead.

The sale event of the summer is here, and the Amazon Prime Day Kindle deals (and other Amazon device deals!) are ones you can count on being great to shop. As someone who tests Amazon gear for a living--Echo speakers, Kindles, Amazon's smart plugs, you name it--I'm always tracking what in Amazon's lineup is worth buying. I have a ton of favorites, from the best of the best, like the Kindle Paperwhite and Echo Show 8, to the Kindle Scribe and Echo Spot. It's the perfect time to shop all of those and more. Not only is everything in this guide WIRED-tested and WIRED-approved, but it's on sale right now.

Amazon Prime Day is four days in 2025, and we're kicking off day three. The Prime Day deals started dropping last month and will end on Friday, July 11. We're working in shifts, covering 20 hours a day through the end, and are dangerously caffeinated--all to help you nab the best Prime Day deals with up-to-date recommendations. The WIRED Reviews team only recommends deals on products we've actually tested and approved, and which are actually discounted. If you're looking for up-to-the-minute coverage of deals, check out our Amazon Prime Day liveblog, which will run from 5 am to midnight daily. If you're coming to Prime Day looking for something dirt-cheap, I've got one for you. Yes, this device is a Chromebook, but as a "Chromebook Plus" model, it's a big step up from the reputation these laptops have when kids are introduced to them in schools. The Acer Chromebook Plus 515 comes with a 1080p display, a spacious 15.6-inch display, and an Intel Core i3 processor. Don't write off a ...

--Federated recommender systems (FedRec) have emerged as a promising solution for delivering personalized recommendations while safeguarding user privacy. However, recent studies have demonstrated their vulnerability to poisoning attacks, wherein adversarial clients can inject carefully crafted updates to manipulate global recommendations. Existing attacks typically target the entire user group, which compromises stealth and increases the risk of detection. In contrast, real-world adversaries may prefer to prompt target items to specific user subgroups, such as recommending health supplements to elderly users, in order to improve attack success rates while preserving stealth. Motivated by this gap, we introduce Spattack, the first targeted poisoning attack designed to manipulate recommendations for specific user subgroups in the federated setting. Specifically, Spattack adopts a two-stage approximation-and-promotion strategy, which first simulates user embeddings of target/non-target subgroups and then prompts target items to the target subgroups. T o enhance the approximation stage, we push the inter-group embeddings away based on contrastive learning and augment the target group's relevant item set based on clustering. T o enhance the promotion stage, we further propose to adaptively tune the optimization weights between target and non-target subgroups. Besides, an embedding alignment strategy is proposed to align the embeddings between the target items and the relevant items. We conduct comprehensive experiments on three real-world datasets, comparing Spattack against seven state-of-the-art poisoning attacks and seven representative defense mechanisms. Experimental results demonstrate that Spattack consistently achieves strong manipulation performance on the specific user subgroup, while incurring minimal impact on non-target users, even when only 0.1% of users are malicious. Moreover, Spattack maintains competitive overall recommendation performance and exhibits strong resilience against existing mainstream defenses.