Customer-obsessed science


Research areas
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September 2, 2025Audible's ML algorithms connect users directly to relevant titles, reducing the number of purchase steps for millions of daily users.
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Featured news
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2025Fine-tuning large language models (LLMs) for specific tasks requires diverse, high-quality training data. However, obtaining sufficient relevant data remains a significant challenge. Existing data synthesis methods either depend on extensive seed datasets or struggle to balance task relevance and data diversity. To address these challenges, we propose Attributeguided multI-hop Data Expansion (AIDE), a novel
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2025Today, E-commerce sellers face several key challenges, including difficulties in discovering and effectively utilizing available programs and tools, and struggling to understand and utilize rich data from various tools. We therefore aim to develop Insight Agents (IA), a conversational multi-agent Data Insight system, to provide E-commerce sellers with personalized data and business insights through automated
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SPIE Defense + Commercial Sensing 20252025Transformer models have revolutionized the field of image captioning, offering advanced capabilities through self attention mechanisms that capture intricate visual and textual relationships. This paper presents an innovative approach to applying transformer models for image captioning. Current State-of-the-Art (SOTA) performance has only been achieved by large vision-language models (LVLMs). Our approach
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NAACL 2025 Workshop on Knowledge-Augmented NLP2025In Recommender Systems, users often seek the best products through indirect, vague, or under-specified queries, such as “best shoes for trail running”. Such queries, also referred to as implicit superlative queries, pose a significant challenge for standard retrieval and ranking systems as they lack an explicit mention of attributes and require identifying and reasoning over complex factors. We investigate
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The Web Conf 2025 Workshop on Resource-Efficient Learning for the Web2025E-commerce has experienced significant growth recently, generating vast amounts of data on user preferences, interactions, and purchase patterns. Effectively modeling and representing users and products in these online ecosystems is crucial for various applications. However, existing approaches for e-commerce representation learning face several limitations: (i) they primarily consider user behavior patterns
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