Qilin: A Multimodal Information Retrieval Dataset with APP-level User
  Sessions
Authors: name: Jia Chen, name: Qian Dong, name: Haitao Li, name: Xiaohui He, name: Yan Gao, name: Shaosheng Cao, name: Yi Wu, name: Ping Yang, name: Chen Xu, name: Yao Hu, name: Qingyao Ai, name: Yiqun Liu

User-generated content (UGC) communities, especially those featuring
multimodal content, improve user experiences by integrating visual and textual
information into results (or items). The challenge of improving user
experiences in complex systems with search and recommendation (S\&R) services
has drawn significant attention from both academia and industry these years.
However, the lack of high-quality datasets has limited the research progress on
multimodal S\&R. To address the growing need for developing better S\&R
services, we present a novel multimodal information retrieval dataset in this
paper, namely Qilin. The dataset is collected from Xiaohongshu, a popular
social platform with over 300 million monthly active users and an average
search penetration rate of over 70\%. In contrast to existing datasets,
Qilin offers a comprehensive collection of user sessions with
heterogeneous results like image-text notes, video notes, commercial notes, and
direct answers, facilitating the development of advanced multimodal neural
retrieval models across diverse task settings. To better model user
satisfaction and support the analysis of heterogeneous user behaviors, we also
collect extensive APP-level contextual signals and genuine user feedback.
Notably, Qilin contains user-favored answers and their referred results for
search requests triggering the Deep Query Answering (DQA) module. This allows
not only the training \& evaluation of a Retrieval-augmented Generation (RAG)
pipeline, but also the exploration of how such a module would affect users'
search behavior. Through comprehensive analysis and experiments, we provide
interesting findings and insights for further improving S\&R systems. We hope
that Qilin will significantly contribute to the advancement of
multimodal content platforms with S\&R services in the future.