arXiv:2601. 05261v2 Announce Type: replace-cross Abstract: Online consumer reviews are important decision-support resources in e-commerce, yet the increasing volume of reviews often creates information overload and makes it difficult for users to identify content that matches their individual preferences.
arXiv:2603. 29247v3 Announce Type: replace-cross Abstract: LLM-based shopping agents increasingly rely on long purchase histories and multi-turn interactions for personalization, yet naively appending raw history to prompts is often ineffective due to noise, length, and relevance mismatch.
arXiv:2607. 10825v1 Announce Type: cross Abstract: Opinionated text - spanning product reviews, hotel feedback, and social posts - captures rich signals about user experiences, preferences, and concerns.
arXiv:2606. 16973v1 Announce Type: cross Abstract: Incorporating textual reviews into a Recommender System has become a prominent strategy for enriching collaborative signals with semantic information.
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...
The paper introduces CLARER, a recommendation model that fuses aspect features extracted from textual reviews with rating data to enhance recommendation accuracy and explainability. It learns user and item representations by combining rating-based features via an MLP and aspect-based features via a transformer encoder with contrastive learning. A transformer decoder then generates explanations using the combined representations, and experiments on three benchmark datasets show superior performance over baseline methods in both recommendation accuracy and explanation generation.
arXiv:2508.01128v2 Announce Type: replace-cross Abstract: Textual reviews enrich recommender systems with fine-grained preference signals and enhanced explainability. However, in real-world scenarios...
The paper introduces PLUS, a framework that uses reinforcement learning to generate text-based summaries of individual users’ preferences, characteristics, and past conversations. These summaries condition a reward model, allowing it to predict personalized response preferences and improving reward accuracy by 11–77 % over the standard Bradley‑Terry model. PLUS demonstrates robust performance with new users and topics, achieves a 25 % improvement over existing personalized RLHF techniques, and enables zero‑shot personalization for state‑of‑the‑art models like GPT‑4.
arXiv:2608. 04307v1 Announce Type: cross Abstract: Text summarization is deceptively difficult.
arXiv:2609.24028v1 Announce Type: new Abstract: Generating coherent meta-reviews from multiple peer reviews is challenging when reviewer evidence conflicts and varies in reliability. Existing approac...
arXiv:2608. 11604v1 Announce Type: new Abstract: Large language model-based shopping agents are increasingly deployed in real-world e-commerce platforms, generating massive amounts of user interaction logs that provide valuable supervision for improving these agents.
HyperTrace is a training‑free framework that personalizes large language models by tracing latent user preferences online. It maintains interpretable natural‑language hypotheses about short‑term intent and long‑term preferences, updating them with an SMC‑style reweighting process driven by an LLM‑based surrogate choice model. Experiments on PRISM and PersonaMem‑v2 demonstrate that HyperTrace improves response alignment, preference prediction, and profile consistency compared to strong online baselines.