We present ReSuMe, a general framework for mutual enhancement of dense retrieval systems and document summarizers through reinforcement learning. The framework jointly optimizes a language model for generating retrieval-oriented summaries and adapts the retrieval model to these summaries through alternating fine-tuning phases. We employ Group Relative Policy Optimization (GRPO) to fine-tune the language model based on retrieval relevance rather than linguistic quality alone, while the retrieval model is iteratively updated using contrastive learning on the generated summaries. This co-optimization process addresses the fundamental distribution shift problem that arises when retrieval models trained on full documents must operate on synthetic summaries during inference. By progressively reducing this distribution gap, our framework yields two key benefits: improved retrieval performance and a high-quality document summarizer optimized for retrieval tasks. We demonstrate our framework using Contriever on the MS-MARCO dataset, achieving consistent improvements of 13.2% in MRR@10 and 6.7% in Recall@100 over the baseline. The framework is model-agnostic and can be applied to enhance any dense retrieval system while simultaneously producing an effective document summarization model.

Semantic matching is an important component of a product search pipeline. Its goal is to capture the semantic intent of the search query as opposed to the syntactic matching performed by a lexical matching system. A semantic matching model captures relationships like synonyms, and also captures common behavioral patterns to retrieve relevant results by generalizing from purchase data. They however suffer from lack of availability of informative negative examples for model training. Various methods have been proposed in the past to address this issue based upon hard-negative mining and contrastive learning.

Lack of calibrated product sizing in popular categories such as apparel and shoes leads to customers purchasing incorrect sizes, which in turn results in high return rates due to fit issues. We address the problem of product size recommendations based on customer purchase and return data. We propose a novel approach based on Bayesian logit and probit regression models with ordinal categories Small, Fit, Largeto model size fits as a function of the difference between latent sizes of customers and products. We propose posterior computation based on mean-field variational inference, leveraging the Polya-Gamma augmentation for the logit prior, that results in simple updates, enabling our technique to efficiently handle large datasets. Our Bayesian approach effectively deals with issues arising from noise and sparsity in the data providing robust recommendations. Offline experiments with real-life shoe datasets show that our model outperforms the state-of-the-art in 5 of 6 datasets. and leads to an improvement of 17-26% in AUC over baselines when predicting size fit outcomes.

We propose a novel latent factor model for recommending product size fits {Small, Fit, Large} to customers. Latent factors for customers and products in our model correspond to their physical true size, and are learnt from past product purchase and returns data. The outcome for a customer, product pair is predicted based on the difference between customer and product true sizes, and efficient algorithms are proposed for computing customer and product true size values that minimize two loss function variants. In experiments with Amazon shoe datasets, we show that our latent factor models incorporating personas, and leveraging return codes show a 17-21% AUC improvement compared to baselines. In an online A/B test, our algorithms show an improvement of 0.49% in percentage of Fit transactions over control.