In e-commerce search engines, query rewriting (QR) is a crucial technique that improves shopping experience by reducing the vocabulary gap between user queries and product catalog. Recent works have mainly adopted the generative paradigm. However, they hardly ensure high-quality generated rewrites and do not consider personalization, which leads to degraded search relevance. In this work, we present Contrastive Learning Enhanced Query Rewriting (CLE-QR), the solution used in Taobao product search. It uses a novel contrastive learning enhanced architecture based on "query retrieval-semantic relevance ranking-online ranking". It finds the rewrites from hundreds of millions of historical queries while considering relevance and personalization. Specifically, we first alleviate the representation degeneration problem during the query retrieval stage by using an unsupervised contrastive loss, and then further propose an interaction-aware matching method to find the beneficial and incremental candidates, thus improving the quality and relevance of candidate queries. We then present a relevance-oriented contrastive pre-training paradigm on the noisy user feedback data to improve semantic ranking performance. Finally, we rank these candidates online with the user profile to model personalization for the retrieval of more relevant products. We evaluate CLE-QR on Taobao Product Search, one of the largest e-commerce platforms in China. Significant metrics gains are observed in online A/B tests. CLE-QR has been deployed to our large-scale commercial retrieval system and serviced hundreds of millions of users since December 2021. We also introduce its online deployment scheme, and share practical lessons and optimization tricks of our lexical match system.

As a kind of erasure code, piggybacking has been applied in practice since it can significantly reduce the repair bandwidth of distributed storage systems. Currently, several efficient piggybacking designs have been proposed. In this paper, we propose a more efficient “one-to-one” piggybacking design (OOP) to further reduce the repair bandwidth. Different from the existing piggybacking designs, OOP adopts a simple encoding principle that one parity node only piggybacks symbols from one substripe. Particularly, OOP takes into account the efficient repair of systematic nodes and parity nodes simultaneously. It is shown that for OOP design, the optimal number of substripes is (√r-1+r-1), the average repair bandwidth ratio of systematic nodes can be as low as 2√r-1+1/2√r-1+r, and the average repair bandwidth ratio of parity nodes reaches √r-1+r-1/r + (r-1)2-√(r-1)3rk). In contrast to the existing piggybacking designs, OOP can further reduce the repair bandwidth of both system nodes and parity nodes.