mlsanityData to 7 Oct 2026

Joint Deep Modeling of Users and Items Using Reviews for Recommendation

arXiv 1701.04783 ↗First posted 17 Jan 2017cs.LG cs.IRcited by 84

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Abstract (arXiv)

A large amount of information exists in reviews written by users. This source of information has been ignored by most of the current recommender systems while it can potentially alleviate the sparsity problem and improve the quality of recommendations. In this paper, we present a deep model to learn item properties and user behaviors jointly from review text. The proposed model, named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel neural networks coupled in the last layers. One of the networks focuses on learning user behaviors exploiting reviews written by the user, and the other one learns item properties from the reviews written for the item. A shared layer is introduced on the top to couple these two networks together. The shared layer enables latent factors learned for users and items to interact with each other in a manner similar to factorization machine techniques. Experimental results demonstrate that DeepCoNN significantly outperforms all baseline recommender systems on a variety of datasets.

Key facts. Cited by 84 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jan 2017.

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Citing papers per year: 2021: 15; 2022: 13; 2023: 16; 2024: 14; 2025: 15; 2026: 11.

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  1. 2022Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)cited by 396
  2. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 188
  3. 2023How to Index Item IDs for Recommendation Foundation Modelscited by 102
  4. 2022Recommendation Unlearningcited by 71
  5. 2021A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendationcited by 59
  6. 2025RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMscited by 58
  7. 2025Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representationscited by 35
  8. 2021Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Networkcited by 28
  9. 2021Generate Natural Language Explanations for Recommendationcited by 22
  10. 2024MMREC: LLM Based Multi-Modal Recommender Systemcited by 22
  11. 2022Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendationcited by 14
  12. 2023Recent Developments in Recommender Systems: A Surveycited by 14
38 more
  1. 2021Causal Collaborative Filteringcited by 13
  2. 2021T-RECS: A Simulation Tool to Study the Societal Impact of Recommender Systemscited by 12
  3. 2024Triple Modality Fusion: Aligning Visual, Textual, and Graph Data with Large Language Models for Multi-Behavior Recommendationscited by 9
  4. 2023Shilling Black-box Review-based Recommender Systems through Fake Review Generationcited by 8
  5. 2022Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendationscited by 6
  6. 2022Personality-Driven Social Multimedia Content Recommendationcited by 6
  7. 2024Enhancing Content-based Recommendation via Large Language Modelcited by 6
  8. 2024TEARS: Textual Representations for Scrutable Recommendationscited by 5
  9. 2021TLSAN: Time-aware Long- and Short-term Attention Network for Next-item Recommendationcited by 4
  10. 2022A Survey on Dropout Methods and Experimental Verification in Recommendationcited by 4
  11. 2024Review-Incorporated Model-Agnostic Profile Injection Attacks on Recommender Systemscited by 4
  12. 2024SocialRec: User Activity Based Post Weighted Dynamic Personalized Post Recommendation System in Social Mediacited by 4
  13. 2021Augmenting the User-Item Graph with Textual Similarity Modelscited by 3
  14. 2021Dynamic Review-based Recommenderscited by 3
  15. 2023Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filteringcited by 3
  16. 2025Retrieval-Augmented Review Generation for Poisoning Recommender Systemscited by 3
  17. 2021Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendationcited by 2
  18. 2023Knowledge-aware Collaborative Filtering with Pre-trained Language Model for Personalized Review-based Rating Predictioncited by 2
  19. 2023GPT as a Baseline for Recommendation Explanation Textscited by 2
  20. 2024Topic-Aware Knowledge Graph with Large Language Models for Interoperability in Recommender Systemscited by 2
  21. 2025Counterfactual Inference for Eliminating Sentiment Bias in Recommender Systemscited by 2
  22. 2025A Framework for Generating Conversational Recommendation Datasets from Behavioral Interactionscited by 2
  23. 2025Learning to Shop Like Humans: A Review-driven Retrieval-Augmented Recommendation Framework with LLMscited by 2
  24. 2025HADSF: Aspect Aware Semantic Control for Explainable Recommendationcited by 2
  25. 2021Dual-embedding based Neural Collaborative Filtering for Recommender Systemscited by 1
  26. 2021A Hybrid Recommender System for Recommending Smartphones to Prospective Customerscited by 1
  27. 2021Learning Complex Users' Preferences for Recommender Systemscited by 1
  28. 2021SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item Recommendationcited by 1
  29. 2022On Faithfulness and Coherence of Language Explanations for Recommendation Systemscited by 1
  30. 2023KNNs of Semantic Encodings for Rating Predictioncited by 1
  31. 2023Creating Synthetic Datasets for Collaborative Filtering Recommender Systems using Generative Adversarial Networkscited by 1
  32. 2023Attentive Graph-based Text-aware Preference Modeling for Top-N Recommendationcited by 1
  33. 2023Empowering Few-Shot Recommender Systems with Large Language Models -- Enhanced Representationscited by 1
  34. 2024USE: Dynamic User Modeling with Stateful Sequence Modelscited by 1
  35. 2025Prompt-Based LLMs for Position Bias-Aware Reranking in Personalized Recommendationscited by 1
  36. 2025Transforming User Defined Criteria into Explainable Indicators with an Integrated LLM AHP Systemcited by 1
  37. 2026TRACE: Tourism Recommendation with Accountable Citation Evidencecited by 1
  38. 2026Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedbackcited by 1

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