arXiv AI
Why This, Not That? Mining User Profiles for Pair-wise Counterfactuals
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arXiv AI
Oct 28
Counterfactual Profile Perturbations for Recommendation Explanations: A Reproducibility Study
This reproducibility study investigates the counterfactual explanation method Learning to eXplain Recommendations (LXR) by extending its application beyond the top‑ranked item to other recommended items. The authors compare LXR against alternative baselines such as item popularity and latent factor similarity, noting that these baselines perform surprisingly well on certain tasks. They also analyze how item popularity affects explanation quality, finding that LXR produces shorter perturbations for less popular items, and conduct a brute‑force search to estimate a lower bound on optimal perturbation size.
arXiv AI
Aug 24
Profiling What Matters: Context-Aware Item Profiles from Large-Scale Metadata for LLM Recommenders
arXiv:2608.20801v1 Announce Type: cross Abstract: While Large Language Models (LLMs) have significantly advanced reranking in recommendation, effectively leveraging item-side information remains chal...
arXiv AI
Oct 5
Reasoning with Evidence, Not Merely Rationales: Verifiable Preference Proofs for LLM-Based Recommendation
The paper introduces PROVE-REC, a two‑pass framework that generates verifiable preference proofs for large language model (LLM) recommendation systems. In Pass A, the model condenses user interaction histories into a compact proof of positive and avoidance claims linked to specific evidence entries. Pass B then uses only this proof and its evidence to predict the next item, ensuring the recommendation follows the reasoning path. Verification steps compare masked evidence and removed claims to confirm grounding and influence, while a ranking‑preservation objective retains useful historical information. Experiments on diverse real‑world datasets show PROVE‑REC outperforms strong baselines by up to 7.45%, producing claims that are both better grounded and more influential to recommendation quality.
arXiv AI
Sep 10
Neutralizing Popularity Bias in LLM-based Recommendation via Counterfactual Reasoning Guidelines
The paper introduces NPRec, a model‑agnostic framework that uses counterfactual reasoning to neutralize popularity bias in large language model–based recommender systems. By generating debiased textual guidelines that separate intrinsic user interests from popularity signals, NPRec injects these guidelines at inference time to guide the LLM’s generation without updating parameters. Experiments on three real‑world datasets show improved recommendation accuracy, explanation quality, and debiasing performance.
arXiv AI
Sep 3
The Utility of LLMs in Recommender Systems Explanation Evaluation
The paper investigates how large language models (LLMs) can evaluate explanations in recommender systems. It generates 18 explanation prototypes and has 14 LLMs rate them, comparing the results to human ratings from a user study. Findings show that while LLMs mimic human rating patterns and correlate moderately with human judgments, their absolute agreement is low and varies with model size and evaluation design, leading to four practical recommendations for using LLMs in this context.
arXiv AI
Sep 18
FacetCRS: Multi-Faceted Preference Learning for Pricking Filter Bubbles in Conversational Recommender System
FacetCRS is a conversational recommender system that tackles the filter‑bubble problem by learning multi‑faceted user preferences—entity, word, context, and review facets—through natural language interactions. The framework adaptively models these preference facets and incorporates external knowledge to provide diverse recommendations. Experiments on two benchmark datasets show that FacetCRS outperforms existing methods in reducing filter bubbles and improving recommendation quality.