skelts

4 ms·

1.
SQL for RAG(docs.shaped.ai)
1 points by skelts 9mo ago|0 comments
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by skelts 2y ago
Building effective recommendation and search systems means going beyond simply predicting relevance. Modern users expect personalized experiences that cater to a wide range of needs and preferences, and businesses need systems that align wi
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by skelts 2y ago
Recommender system benchmarks are less reliable than they seem—this study shows that changing data splitting strategies can significantly alter model rankings. Evaluating 17 state-of-the-art models across different splits (Leave One Last It
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by skelts 2y ago
Traditional recommendation systems struggle with long-term user modeling and scalability. EmbSum leverages LLM-driven summarization to precompute rich user and content embeddings, outperforming models like UNBERT and MINER with fewer parame
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by skelts 2y ago
Google Research’s "Titans" introduces a neural long-term memory module that dynamically learns to memorize and retrieve context during inference, addressing Transformers' limitations with fixed-length attention spans. By leve
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by skelts 2y ago
What if your next job application was evaluated not just by qualifications but by an AI that explains its decision better than most hiring managers? The future of AI-driven job recommendations lies not just in matching talent to opportunity
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by skelts 2y ago
Netflix and Cornell University researchers have exposed significant flaws in cosine similarity. Their study reveals that regularization in linear matrix factorization models introduces arbitrary scaling, leading to unreliable or meaningless
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Calibrating Recommendations to Better Match User Interests(shaped.ai)
6 points by skelts 2y ago|2 comments
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by skelts 2y ago
Recommender systems often overfocus on dominant interests, neglecting diversity. Shaped introduces a method to calibrate recommendations using minimum-cost flow optimization, ensuring results reflect the breadth of user preferences. This ap
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Vector Search with OpenAI Embeddings: Lucene Is All You Need(shaped.ai)
9 points by skelts 2y ago|0 comments
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by skelts 3y ago
i've tried a couple and probably prefer Marqo to the rest. definitely the most user-friendly pick of the bunch.
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by skelts 3y ago
+1 to Marqo. It's documents in documents out rather than vectors in vectors out. Much easier end to end.
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by skelts 3y ago
Pretty similar open source project here: https://www.marqo.ai/blog/from-iron-manual-to-ironman-augmen...
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by skelts 3y ago
What is the context here?
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Image search with localization and open-vocabulary reranking(github.com)
3 points by skelts 4y ago|1 comments