Papers with recommender model

2 papers
Aligning Large Language Models for Controllable Recommendations (2024.acl-long)

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Challenge: Existing literature focuses on integrating domain-specific knowledge into LLMs to enhance accuracy using a fixed task template.
Approach: They propose a collection of supervised learning tasks augmented with labels derived from a conventional recommender model to improve LLMs’ proficiency in adhering to recommendation-specific instructions.
Outcome: The proposed approach significantly improves the capability of LLMs to respond to instructions within recommender systems, reducing formatting errors while maintaining a high level of accuracy.
Incorporating Review-missing Interactions for Generative Explainable Recommendation (2025.coling-main)

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Challenge: Existing models of explainable recommendation use user reviews as ground truths, but in practice, a large amount of users may not leave reviews after purchasing items.
Approach: They propose to incorporate user preferences into explainable recommender models by leveraging generative models to predict the missing reviews and then training the model based on all the predicted and original reviews.
Outcome: The proposed model improves the explanation quality on three publicly available datasets.

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