Papers with recommendation process
ReAL: How Can LLMs Simulate the Real Teacher? Retrieval-enhanced Agent for Adaptive Learning (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Prior methods model learner-item interactions based only on ID sequences, leading to insufficient use of both learner and item information. |
| Approach: | They propose a Retrieval-enhanced Agent for Adaptive Learning powered by large language models to simulate teacher decision-making with extensive prior knowledge and teaching experience. |
| Outcome: | The proposed model outperforms existing models on three real-world datasets in both internal and external perspectives. |
Faithfully Explainable Recommendation via Neural Logic Reasoning (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing models for explainable recommendation have neglected faithfulness of KG reasoning . |
| Approach: | They propose to draw on interpretable logical rules to guide path-reasoning process for explanation generation. |
| Outcome: | The proposed method delivers high-quality recommendations and ascertains the faithfulness of the derived explanation. |
Empowering Retrieval-based Conversational Recommendation with Contrasting User Preferences (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing CRSs assume positive and negative user preferences, but assume that the entities in the dialogue history are positive. |
| Approach: | They propose a conversational recommender model that captures user sentiments and uses the reasoning capacity of the LLMs to extract user's hidden preferences. |
| Outcome: | The proposed model outperforms existing methods in three benchmark datasets, improving up to 99.72% in Recall@10. |
Large Language Models for Generative Recommendation: A Survey and Visionary Discussions (2024.lrec-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have revolutionized the field of natural language processing but are not fully able to leverage the generative power of LLM. |
| Approach: | They examine the progress, methods, and future directions of large language models . they examine what generative recommendation is, why RS should advance to generative recommendations . |
| Outcome: | The proposed approach can be simplified to generate recommendations from the entire pool of items. |