[2603.17361] Public Profile Matters: A Scalable Integrated Approach to Recommend Citations in the Wild

Computer Science > Information Retrieval arXiv:2603.17361 (cs) [Submitted on 18 Mar 2026 (v1), last revised 14 Apr 2026 (this version, v2)] Title:Public Profile Matters: A Scalable Integrated Approach to Recommend Citations in the Wild Authors:Karan Goyal, Dikshant Kukreja, Vikram Goyal, Mukesh Mohania View a PDF of the paper titled Public Profile Matters: A Scalable Integrated Approach to Recommend Citations in the Wild, by Karan Goyal and 3 other authors View PDF HTML (experimental) Abstract:Proper citation of relevant literature is essential for contextualising and validating scientific contributions. While current citation recommendation systems leverage local and global textual information, they often overlook the nuances of the human citation behaviour. Recent methods that incorporate such patterns improve performance but incur high computational costs and introduce systematic biases into downstream rerankers. To address this, we propose Profiler, a lightweight, non-learnable module that captures human citation patterns efficiently and without bias, significantly enhancing candidate retrieval. Furthermore, we identify a critical limitation in current evaluation protocol: the systems are assessed in a transductive setting, which fails to reflect real-world scenarios. We introduce a rigorous Inductive evaluation setting that enforces strict temporal constraints, simulating the recommendation of citations for newly authored papers in the wild. Finally, we present DAVINC...