The dominant retrieve-then-rank pipeline in large-scale recommender systems suffers from mis-calibration and engineering overhead due to its architectural split and differing optimization objectives. While recent generative sequence models have shown promise in unifying retrieval and ranking by auto-regressively generating ranked items, existing solutions typically address either personalized search or query-free recommendation, often exhibiting performance trade-offs when attempting to unify both. We introduce SynerGen, a novel generative recommender model that bridges this critical gap by providing a single generative backbone for both personalized search and recommendation, while simultaneously excelling at retrieval and ranking tasks. Trained on behavioral sequences, our decoder-only Transformer leverages joint optimization with InfoNCE for retrieval and a hybrid pointwise-pairwise loss for ranking, allowing semantic signals from search to improve recommendation and vice versa. We also propose a novel time-aware rotary positional embedding to effectively incorporate time information into the attention mechanism. SynerGen achieves significant improvements on widely adopted recommendation and search benchmarks compared to strong generative recom-mender and joint search and recommendation baselines. This work demonstrates the viability of a single generative foundation model for industrial-scale unified information access. Large-scale search and recommendation systems in e-commerce, short video, and food-delivery platforms are typically deployed as multi-stage cascades.

As large language models (LLMs) become increasingly integrated into daily life, there is growing demand for AI assistants that are not only reactive but also proactive and personalized. While recent advances have pushed forward proactivity and personalization individually, their combination remains underexplored. To bridge this gap, we introduce ProPerSim, a new task and simulation framework for developing assistants capable of making timely, personalized recommendations in realistic home scenarios. In our simulation environment, a user agent with a rich persona interacts with the assistant, providing ratings on how well each suggestion aligns with its preferences and context. The assistant's goal is to use these ratings to learn and adapt to achieve higher scores over time. Built on ProPerSim, we propose ProPerAssistant, a retrieval-augmented, preference-aligned assistant that continually learns and adapts through user feedback. Experiments across 32 diverse personas show that ProPerAssistant adapts its strategy and steadily improves user satisfaction, highlighting the promise of uniting proactivity and personalization.

Aligning AI systems with human privacy preferences requires understanding individuals' nuanced disclosure behaviors beyond general norms. Yet eliciting such boundaries remains challenging due to the context-dependent nature of privacy decisions and the complex trade-offs involved. We present an AI-powered elicitation approach that probes individuals' privacy boundaries through a discriminative task. We conducted a between-subjects study that systematically varied communication roles and delegation conditions, resulting in 1,681 boundary specifications from 169 participants for 61 scenarios. We examined how these contextual factors and individual differences influence the boundary specification. Quantitative results show that communication roles influence individuals' acceptance of detailed and identifiable disclosure, AI delegation and individuals' need for privacy heighten sensitivity to disclosed identifiers, and AI delegation results in less consensus across individuals. Our findings highlight the importance of situating privacy preference elicitation within real-world data flows. We advocate using nuanced privacy boundaries as an alignment goal for future AI systems.

Connecting conversation with external domain knowledge is vital for conversational recommender systems (CRS) to correctly understand user preferences. However, existing solutions either require domain-specific engineering, which limits flexibility, or rely solely on large language models, which increases the risk of hallucination. While Retrieval-Augmented Generation (RAG) holds promise, its naive use in CRS is hindered by noisy dialogues that weaken retrieval and by overlooked nuances among similar items. We propose ReGeS, a reciprocal Retrieval-Generation Synergy framework that unifies generation-augmented retrieval to distill informative user intent from conversations and retrieval-augmented generation to differentiate subtle item features. This synergy obviates the need for extra annotations, reduces hallucinations, and simplifies continuous updates. Experiments on multiple CRS benchmarks show that ReGeS achieves state-of-the-art performance in recommendation accuracy, demonstrating the effectiveness of reciprocal synergy for knowledge-intensive CRS tasks.

When you purchase through links in our articles, we may earn a small commission. Here are the existing Echo smart speakers and displays most in need of a refresh. After skipping last year, Amazon is back with a big fall hardware event slated for next week, and we're expecting plenty of new Echo smart speakers and displays that make the most of Alexa+, Amazon's AI revamp of the Alexa voice assistant. Plenty of other hardware will also be unwrapped during Amazon's September 30 event in New York City; for example, we're sure to see new Kindle tablets, as well as Fire TV models and perhaps even some Ring cameras. For now, though, we're concentrating on new Echo devices, and there are a few popular Echo speakers and displays that are ripe for an upgrade.

Long-term conversational agents require effective memory management to handle dialogue histories that exceed the context window of large language models (LLMs). Existing methods based on fact extraction or summarization reduce redundancy but struggle to organize and retrieve relevant information across different granularities of dialogue and generated memory. We introduce SGMem (Sentence Graph Memory), which represents dialogue as sentence-level graphs within chunked units, capturing associations across turn-, round-, and session-level contexts. By combining retrieved raw dialogue with generated memory such as summaries, facts and insights, SGMem supplies LLMs with coherent and relevant context for response generation. Experiments on LongMemEval and LoCoMo show that SGMem consistently improves accuracy and outperforms strong baselines in long-term conversational question answering.

This paper analyzes Cross-Entropy (CE) loss in knowledge distillation (KD) for recommender systems. KD for recommender systems targets at distilling rankings, especially among items most likely to be preferred, and can only be computed on a small subset of items. Considering these features, we reveal the connection between CE loss and NDCG in the field of KD. We prove that when performing KD on an item subset, minimizing CE loss maximizes the lower bound of NDCG, only if an assumption of closure is satisfied. It requires that the item subset consists of the student's top items. However, this contradicts our goal of distilling rankings of the teacher's top items. We empirically demonstrate the vast gap between these two kinds of top items. To bridge the gap between our goal and theoretical support, we propose Rejuvenated Cross-Entropy for Knowledge Distillation (RCE-KD). It splits the top items given by the teacher into two subsets based on whether they are highly ranked by the student. For the subset that defies the condition, a sampling strategy is devised to use teacher-student collaboration to approximate our assumption of closure. We also combine the losses on the two subsets adaptively. Extensive experiments demonstrate the effectiveness of our method. Our code is available at https://anonymous.4open.science/r/RCE-KD.

When you purchase through links in our articles, we may earn a small commission. Perplexity Email Assistant is like a virtual assistant for your inbox. AI company Perplexity, best known for its free AI-powered answer engine, is now launching another AI-powered tool called Perplexity Email Assistant . This AI assistant integrates directly into your email inbox and can help you maintain better control over your email. Email Assistant can write email drafts in your own tone and conversational style, organize messages, suggest meeting times, and even participate in email threads to save you time on long exchanges.

'ChatGPT, what stocks should I buy?' AI fuels boom in robo-advisory market Stock picking using ChatGPT requires some financial knowledge and even its adopters say there is a high risk of getting it wrong before getting it right. LONDON - As ChatGPT nears its third birthday, at least one in 10 retail investors is using a chatbot to pick stocks, fueling a boom in the robo-advisory market, but even fans say it is a high-risk strategy that cannot replace traditional advisers just yet. Thanks to artificial intelligence, anyone can select stocks, monitor them and obtain investment analysis that was once only available to big banks or institutional investors. The robo-advisory market -- which includes all companies providing automated, algorithm-driven financial advice such as fintech, banks and wealth managers -- is forecast to grow to $470.91 billion in revenues in 2029 from $61.75 billion last year, marking a roughly 600% increase, according to data analysis firm Research and Markets. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

'People say I come across as incredibly boring!' How to find love on the dating apps - whatever the obstacles Sick of swiping and messaging but never meeting anyone you like and who likes you back? Here's what worked for some lucky couples U sing dating apps to find love is commonplace these days - and yet, for many singles, it has become a double-edged sword. The perks of having a never-ending supply of potential matches at your fingertips are obvious - but the appeal of connecting and meeting with strangers is time-limited. It can be especially frustrating to feel as if you're stuck at the swiping stage. In 2023, US jeweller Shane Company found that the average American will spend about eight months using dating apps - swiping on around 3,960 profiles - before finding a partner.