User behavior and experience are important for improving information retrieval (IR) systems. While much research has focused on traditional IR systems, few studies have systematically examined user behavior and search experience with emerging generative IR systems. A key reason for this gap is the lack of publicly available toolkits to record user behavior and feedback in generative IR systems. We developed a comprehensive platform to collect user behavior and feedback on the generative IR system. This platform consists of: 1) a generative IR system that supports both API-based and customized retrieval-augmented generation (RAG) methods, 2) a user interface that logs various user behavior, including prompts, clicks, mouse movements, and scrolling, and 3) an annotation website that allows users to provide feedback. We believe the proposed platform has the potential to streamline data collection for user studies on generative IR systems, paving the way for future research on how users engage with and interact with these systems.

Contrastive learning has shown its potential in many unsupervised tasks, including hashing. However, the representations obtained by contrastive learning generally fail to produce no-table margins between semantic classes. Different semantic samples around the boundary are likely to collide into the same hash code. In this paper, we propose a novel Semantic Centralized Contrastive Hashing (SCCH) to allow the learned features closer to their semantic centers and more applicable to hashing. Specifically, a semantic centralization strategy is proposed by pulling strongly augmented samples towards weakly augmented ones since the weak are closer to semantic centers than the strong. Moreover, quantization directly after contrastive learning would damage the learned similarity relationship. We provide a solution to eliminate the mismatch of similarity metrics between contrastive learning and hashing mapping. Extensive experiments on three benchmark datasets demonstrate that SCCH outperforms the existing state-of-the-art methods.