# Explainable Recommendation via Multi-Task Learning in Opinionated Text Data

arXiv: https://arxiv.org/abs/1806.03568 · HTML: https://mlsanity.com/paper/1806.03568/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2018.

## Built on by (most cited first, 30 of 40)

- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [LLM-Rec: Personalized Recommendation via Prompting Large Language Models](https://mlsanity.com/paper/2307.15780/) (2023)
- [Counterfactual Explainable Recommendation](https://mlsanity.com/paper/2108.10539/) (2021)
- [Generate Natural Language Explanations for Recommendation](https://mlsanity.com/paper/2101.03392/) (2021)
- [Cross-Task Knowledge Distillation in Multi-Task Recommendation](https://mlsanity.com/paper/2202.09852/) (2022)
- [Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters](https://mlsanity.com/paper/2111.05897/) (2021)
- [Explainability in Music Recommender Systems](https://mlsanity.com/paper/2201.10528/) (2022)
- [Model Explainability in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/2106.07410/) (2021)
- [Measuring "Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation](https://mlsanity.com/paper/2202.06466/) (2022)
- [Explanation as a Defense of Recommendation](https://mlsanity.com/paper/2101.09656/) (2021)
- [From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems](https://mlsanity.com/paper/2110.14844/) (2021)
- [Comparative Explanations of Recommendations](https://mlsanity.com/paper/2111.00670/) (2021)
- [Rank-LIME: Local Model-Agnostic Feature Attribution for Learning to Rank](https://mlsanity.com/paper/2212.12722/) (2022)
- [COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation](https://mlsanity.com/paper/2210.15500/) (2022)
- [SoREX: Towards Self-Explainable Social Recommendation with Relevant Ego-Path Extraction](https://mlsanity.com/paper/2510.00080/) (2025)
- [Graph-based Extractive Explainer for Recommendations](https://mlsanity.com/paper/2202.09730/) (2022)
- [Knowledge-grounded Natural Language Recommendation Explanation](https://mlsanity.com/paper/2308.15813/) (2023)
- [Counterfactual Collaborative Reasoning](https://mlsanity.com/paper/2307.00165/) (2023)
- [User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations](https://mlsanity.com/paper/2308.00894/) (2023)
- [Counterfactually Evaluating Explanations in Recommender Systems](https://mlsanity.com/paper/2203.01310/) (2022)
- [Towards Explainable Collaborative Filtering with Taste Clusters Learning](https://mlsanity.com/paper/2304.13937/) (2023)
- [Comparing Apples to Apples: Generating Aspect-Aware Comparative Sentences from User Reviews](https://mlsanity.com/paper/2307.03691/) (2023)
- [Efficient Multi-Task Learning via Generalist Recommender](https://mlsanity.com/paper/2504.05318/) (2025)
- [Category Aware Explainable Conversational Recommendation](https://mlsanity.com/paper/2103.08733/) (2021)
- [Deep Latent Emotion Network for Multi-Task Learning](https://mlsanity.com/paper/2104.08716/) (2021)
- [CrystalCandle: A User-Facing Model Explainer for Narrative Explanations](https://mlsanity.com/paper/2105.12941/) (2021)
- [Learning to Counterfactually Explain Recommendations](https://mlsanity.com/paper/2211.09752/) (2022)
- [Explaining Recommendation System Using Counterfactual Textual Explanations](https://mlsanity.com/paper/2303.11160/) (2023)
- [Whom do Explanations Serve? A Systematic Literature Survey of User Characteristics in Explainable Recommender Systems Evaluation](https://mlsanity.com/paper/2412.14193/) (2024)
- [Explainable Recommendation with Simulated Human Feedback](https://mlsanity.com/paper/2504.14147/) (2025)

## Abstract (arXiv)

Explaining automatically generated recommendations allows users to make more informed and accurate decisions about which results to utilize, and therefore improves their satisfaction. In this work, we develop a multi-task learning solution for explainable recommendation. Two companion learning tasks of user preference modeling for recommendation and textitopinionated content modeling for explanation are integrated via a joint tensor factorization. As a result, the algorithm predicts not only a user's preference over a list of items, i.e., recommendation, but also how the user would appreciate a particular item at the feature level, i.e., opinionated textual explanation. Extensive experiments on two large collections of Amazon and Yelp reviews confirmed the effectiveness of our solution in both recommendation and explanation tasks, compared with several existing recommendation algorithms. And our extensive user study clearly demonstrates the practical value of the explainable recommendations generated by our algorithm.