| Challenge: | Existing systems that predict whether a user likes a book are collaborative filtering and content-based recommendation. |
| Approach: | They propose a system that learns authors' style and uses it to recommend books . they also evaluated whether similar books/authors were annotated similarly by experts . |
| Outcome: | The proposed system gives better accuracy when compared with state-of-the-art methods. |
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Detecting, Generating, and Evaluating in the Writing Style of Different Authors (2025.naacl-srw)
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| Challenge: | In recent years, stylometry has been investigated in many different fields. |
| Approach: | They propose to use sentences from different books to generate and evaluate stylistic texts according to the authors' writing styles. |
| Outcome: | The proposed model can detect, generate, and evaluate documents according to the authors' writing styles with unpaired samples. |
Can Authorship Representation Learning Capture Stylistic Features? (2023.tacl-1)
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Andrew Wang, Cristina Aggazzotti, Rebecca Kotula, Rafael Rivera Soto, Marcus Bishop, Nicholas Andrews
| Challenge: | Existing methods to disentangle an author's style from the content of their writing are limited by the reliance on human labels and the narrow focus of stylistic distinctions. |
| Approach: | They propose to use a surrogate task to learn authorship representations that are sensitive to writing style and to validate their hypothesis . |
| Outcome: | The proposed representations are sensitive to writing style and may be robust to topic drift over time. |
Thesis Proposal: Comparing Human and Model Perception of Writing Style under Controlled Perturbations (2026.eacl-srw)
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| Challenge: | This thesis examines how humans and models perceive writing style under controlled perturbations. |
| Approach: | They examine how humans and models perceive writing style under controlled perturbations . they also examine whether perturbations that reduce algorithmic recognition obscure stylistic identity . |
| Outcome: | The proposed research compares models and humans to find out how linguistic cues affect writing style . it will clarify how linguistic cue contributes differently to human and algorithmic perception of style - a cnn.com article argues . |
A Genre-Aware Attention Model to Improve the Likability Prediction of Books (D18-1)
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| Challenge: | Existing methods for likability prediction are time-consuming and too rigid. |
| Approach: | They propose a novel neural architecture that incorporates genre supervision to assign weights to individual feature types based on the characteristics of each book. |
| Outcome: | The proposed method outperforms state-of-the-art methods and achieves competitive results. |
Learning Interpretable Style Embeddings via Prompting LLMs (2023.findings-emnlp)
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| Challenge: | Prior work has treated the style of a text as separable from the content. |
| Approach: | They use prompting to perform stylometry on a large number of texts to generate a synthetic stylometric dataset. |
| Outcome: | The proposed model trains human-interpretable representations on a large stylometric dataset and a linguistic model for style representation learning. |
Topic or Style? Exploring the Most Useful Features for Authorship Attribution (C18-1)
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| Challenge: | Existing approaches to authorship attribution rely on individual's writing style and/or preferred topics. |
| Approach: | They analyse four widely used datasets to explore how different types of features affect authorship attribution accuracy under varying conditions. |
| Outcome: | The proposed model outperforms the state-of-the-art on two out of the four datasets used. |
Latent Space Interpretation for Stylistic Analysis and Explainable Authorship Attribution (2025.coling-main)
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| Challenge: | Recent authorship attribution methods learn authorship representations of text in a latent, uninterpretable space, which hinders their usability in real-world applications. |
| Approach: | They propose a method for interpreting latent authorship representations by identifying representative points in the latent space and leveraging large language models to generate informative natural language descriptions of the writing style associated with each point. |
| Outcome: | The proposed method outperforms baseline methods on the authorship attribution task by +20% on average when aided with explanations from the method. |
Who’s the Author? How Explanations Impact User Reliance in AI-Assisted Authorship Attribution (2025.findings-emnlp)
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| Challenge: | despite growing interest in explainable NLP, it remains unclear how explanation strategies shape user behavior in tasks like authorship identification. |
| Approach: | They propose two explanation types to support their analysis of user behavior . they use example-based style rewrites and feature-based rationales to generate explanations . |
| Outcome: | The proposed explanations support appropriate reliance, whereas explanations increase AI overreliance, the study finds . |
A Text-Based Recommender System that Leverages Explicit Affective State Preferences (2025.emnlp-main)
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| Challenge: | Existing systems that leverage user preferences that are implicit in user-item rating histories can be slow to track changes in user preferences and imprecise for users with diverse preferences. |
| Approach: | They propose a novel recommendation task that leverages a wide range of affective states sought explicitly by the user to identify items that induce those affective state. |
| Outcome: | The proposed model can leverage a wide range of affective states sought explicitly by the user to identify items likely to induce those affective state. |
A Dataset for Expert Reviewer Recommendation with Large Language Models as Zero-shot Rankers (2025.coling-main)
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Vanja M. Karan, Stephen McQuistin, Ryo Yanagida, Colin Perkins, Gareth Tyson, Ignacio Castro, Patrick G.T. Healey, Matthew Purver
| Challenge: | state of the art reviewer recommendation systems still have relatively high error rates . |
| Approach: | They propose to use a large language model to improve on SotA, but not a cure-all . they first create a new dataset and introduce LLMs with prompting to evaluate their performance. |
| Outcome: | The proposed approach improves on SotA but not cure-all, the authors argue . they show that the proposed approach can be extended to many related tasks . |