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Kuaishou Tech
Kuaishou Tech
Apr 24, 2023 · Artificial Intelligence

Divide‑and‑Conquer Embedding‑Based Retrieval with Prompt‑Based Multi‑Task Learning for Large‑Scale Recommendation

This paper identifies the trade‑off between simple and hard negatives in embedding‑based retrieval for recommendation, proposes a clustering‑based divide‑and‑conquer framework combined with prompt‑driven multi‑task learning to improve relevance, diversity, and fairness, and validates the approach through offline metrics, online A/B tests, and comparative experiments.

Recommendation systemsapproximate nearest neighbordivide and conquer
0 likes · 9 min read
Divide‑and‑Conquer Embedding‑Based Retrieval with Prompt‑Based Multi‑Task Learning for Large‑Scale Recommendation
DataFunSummit
DataFunSummit
Jul 25, 2021 · Artificial Intelligence

Advances in Query Understanding and Semantic Retrieval at Zhihu Search

This article details Zhihu Search's engineering solutions for long‑tail query challenges, covering historical development, term weighting, synonym expansion, query rewriting with reinforcement learning, and semantic recall using BERT‑based models, while also outlining future research directions such as GAN‑based rewriting and lightweight pre‑training.

BERTembedding retrievalquery rewriting
0 likes · 14 min read
Advances in Query Understanding and Semantic Retrieval at Zhihu Search