Researchers developed NovelQR, a novel quotation recommendation system focusing on 'unexpected yet rational' quotes
User studies show people consistently prefer novel yet contextually appropriate quotations
Existing models struggle to understand the deep meanings of quotations
NovelQR outperforms baselines in recommending engaging and appropriate quotations
📖 Full Retelling
Researchers Bowei Zhang, Jin Xiao, Guanglei Yue, Qianyu He, Yanghua Xiao, Deqing Yang, and Jiaqing Liang introduced a novel quotation recommendation system called NovelQR on December 15, 2025, through arXiv, addressing the limitation of existing systems that optimize for surface-level topical relevance while ignoring deeper semantic and aesthetic properties that make quotations memorable. The research paper, titled 'What Makes an Ideal Quote? Recommending 'Unexpected yet Rational' Quotations via Novelty,' begins with two key empirical observations. First, a systematic user study revealed that people consistently prefer quotations that are 'unexpected yet rational' in context, identifying novelty as a crucial element. Second, the researchers found that existing models struggle to fully comprehend the deep meanings of quotations. Drawing inspiration from defamiliarization theory, the team formalized quote recommendation as selecting contextually novel yet semantically coherent quotations. To implement this approach, the researchers developed NovelQR, a novelty-driven quotation recommendation framework that employs a generative label agent to interpret quotations and context into multi-dimensional deep-meaning labels, enabling enhanced retrieval, while a token-level novelty estimator reranks candidates to mitigate auto-regressive continuation bias.
🏷️ Themes
Information Retrieval, Artificial Intelligence, Computational Linguistics
Novelty (derived from Latin word novus for "new") is the quality of being new, or following from that, of being striking, original or unusual. Novelty may be the shared experience of a new cultural phenomenon or the subjective perception of an individual.
From the meaning of being unusual, usage is ...
Information retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an information need. The information need can be specified in the form of a search query. In the case of document retrieval, queries can be base...
Use of computational tools for the study of linguistics
Computational linguistics is an interdisciplinary field concerned with the computational modelling of natural language, as well as the study of appropriate computational approaches to linguistic questions. In general, computational linguistics draws upon linguistics, computer science, artificial int...
# Artificial Intelligence (AI)
**Artificial Intelligence (AI)** is a specialized field of computer science dedicated to the development and study of computational systems capable of performing tasks typically associated with human intelligence. These tasks include learning, reasoning, problem-solvi...
No entity connections available yet for this article.
Original Source
--> Computer Science > Information Retrieval arXiv:2602.22220 [Submitted on 15 Dec 2025] Title: What Makes an Ideal Quote? Recommending "Unexpected yet Rational" Quotations via Novelty Authors: Bowei Zhang , Jin Xiao , Guanglei Yue , Qianyu He , Yanghua Xiao , Deqing Yang , Jiaqing Liang View a PDF of the paper titled What Makes an Ideal Quote? Recommending "Unexpected yet Rational" Quotations via Novelty, by Bowei Zhang and 5 other authors View PDF HTML Abstract: Quotation recommendation aims to enrich writing by suggesting quotes that complement a given context, yet existing systems mostly optimize surface-level topical relevance and ignore the deeper semantic and aesthetic properties that make quotations memorable. We start from two empirical observations. First, a systematic user study shows that people consistently prefer quotations that are ``unexpected yet rational'' in context, identifying novelty as a key desideratum. Second, we find that strong existing models struggle to fully understand the deep meanings of quotations. Inspired by defamiliarization theory, we therefore formalize quote recommendation as choosing contextually novel but semantically coherent quotations. We operationalize this objective with NovelQR, a novelty-driven quotation recommendation framework. A generative label agent first interprets each quotation and its surrounding context into multi-dimensional deep-meaning labels, enabling label-enhanced retrieval. A token-level novelty estimator then reranks candidates while mitigating auto-regressive continuation bias. Experiments on bilingual datasets spanning diverse real-world domains show that our system recommends quotations that human judges rate as more appropriate, more novel, and more engaging than other baselines, while matching or surpassing existing methods in novelty estimation. Comments: 36 pages, 16 figures and 13 tables Subjects: Information Retrieval (cs.IR) ; Artificial Intelligence (cs.AI); Computation and Language (cs.CL)...
