Challenge: Existing LLMs are opaque and difficult to interpret, resulting in limited interpretability.
Approach: They propose an interaction-aware profile generator that jointly produces user and item profiles conditioned on both user history and item evidence.
Outcome: The proposed model outperforms baselines on three real-world datasets.

Similar Papers

From ID to LLM: Rethinking Representation Learning for Recommendation (2026.acl-long)

Copied to clipboard

Challenge: Recent studies indicate a fundamental incompatibility between ID representations and language model (LM) representations as they capture behavioral and semantic spaces respectively.
Approach: They propose a Profile-then-Embedding framework for recommendation that integrates semantic user and item profiles and a Personalized Embedded stage to encode these profiles into task-aligned recommendation embeddings.
Outcome: The proposed framework achieves significant gains across three benchmark datasets, including cold-start and long-tail scenarios.
Exploring Unified Training Framework for Multimodal User Profiling (2025.coling-main)

Copied to clipboard

Challenge: Recent studies on user profiling focus on extracting multiple aspects of user attributes from textual reviews, but these studies do not fully exploit the potential of the rich multimodal data at hand.
Approach: They propose a task that utilizes both review texts and their accompanying images to generate comprehensive user profiles.
Outcome: The proposed training framework incorporates historical review texts and images for user profile generation.
PRODIGy: a PROfile-based DIalogue Generation dataset (2024.findings-naacl)

Copied to clipboard

Challenge: Existing profiles-based dialogue datasets lack explicit profile representations or are difficult to collect.
Approach: They propose a dataset that brings together multiple profiles for each speaker, and then integrates them together to provide a more comprehensive profile dimension set for generative language models.
Outcome: The PRODIGy dataset provides a more comprehensive profile dimension set for each speaker.
PersonaX: A Recommendation Agent-Oriented User Modeling Framework for Long Behavior Sequence (2025.findings-acl)

Copied to clipboard

Challenge: Existing methods for user profile modeling extract only partial segments from full historical behavior sequence, resulting in incomplete modeling and suboptimal profiling.
Approach: They propose an agent-agnostic LLM-UM framework to augment downstream recommendation agents . it segments complete historical behaviors into clustered groups and performs offline multi-persona profiling .
Outcome: The proposed framework improves agent performance and inference efficiency by 31% and 10% using 30–50% of behavioral data.
Rethinking LLM-Based Recommendations: A Personalized Query-Driven Parallel Integration (2025.findings-emnlp)

Copied to clipboard

Challenge: Query-to-Recommendation framework integrates large langucage models into recommendation systems . but it faces training-induced bias and bottlenecks from serialized architecture .
Approach: They propose a parallel recommendation framework that decouples LLMs from candidate pre-selection and direct retrieval over the entire item pool.
Outcome: The proposed framework decouples LLMs from candidate pre-selection and enables direct retrieval over the entire item pool.
Profile Consistency Identification for Open-domain Dialogue Agents (2020.emnlp-main)

Copied to clipboard

Challenge: Existing studies on improving attribute consistency focus on incorporating attribute information in responses, but few efforts have identified the consistency relations between response and attribute profile.
Approach: They propose a key-value structure information enriched BERT model to identify the profile consistency . they propose to incorporate attribute information into the generated responses .
Outcome: The proposed model improves over strong baselines on downstream tasks.
If You Want to Go Far Go Together: Unsupervised Joint Candidate Evidence Retrieval for Multi-hop Question Answering (2021.naacl-main)

Copied to clipboard

Challenge: et al. : evidence retrieval is highly dependent on partial, incorrect or no supporting knowledge.
Approach: They propose a method that retrieves and reranks evidence facts jointly . they propose to account for links between sentences and coverage with the given query .
Outcome: The proposed approach achieves state-of-the-art evidence retrieval performance on two multi-hop question answering datasets.
Beyond Overlap Metrics: Rewarding Reasoning and Preferences for Faithful Multi-Role Dialogue Summarization (2026.findings-acl)

Copied to clipboard

Challenge: Existing methods for multi-role dialogue summarization favor surface-level imitation of references rather than genuine gains in faithfulness or alignment with human preferences.
Approach: They propose a framework that couples explicit cognitive-style reasoning with reward-based optimization for multi-role dialogue summarization.
Outcome: The proposed framework matches strong baselines on ROUGE and BERTScore, while in-depth analysis on SAMSum shows clear gains in factual faithfulness and model-based preference alignment.
Know You First and Be You Better: Modeling Human-Like User Simulators via Implicit Profiles (2025.acl-long)

Copied to clipboard

Challenge: Existing user simulators lack authenticity and user-level diversity in interactions with large language models.
Approach: They propose a user simulator with implicit user profiles that infers user profiles from human-machine interactions to simulate personalized and realistic dialogues.
Outcome: The proposed framework outperforms baselines in authenticity and diversity while maintaining comparable consistency.
Exploiting Rich Textual User-Product Context for Improving Personalized Sentiment Analysis (2023.findings-acl)

Copied to clipboard

Challenge: Typical approaches do not exploit the potential of historical reviews or do not make full use of user/product associations.
Approach: They propose to use historical reviews to initialize user and product representations and incorporate textual associations via a user-product cross-context module.
Outcome: The proposed method outperforms existing state-of-the-art models on IMDb, Yelp and Longformer benchmarks.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations