# Try This Instead: Personalized and Interpretable Substitute Recommendation

arXiv: https://arxiv.org/abs/2005.09344 · HTML: https://mlsanity.com/paper/2005.09344/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2020.

## Built on by (most cited first, 30 of 34)

- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [Counterfactual Explainable Recommendation](https://mlsanity.com/paper/2108.10539/) (2021)
- [Contrastive Self-supervised Sequential Recommendation with Robust Augmentation](https://mlsanity.com/paper/2108.06479/) (2021)
- [Learning to Ask Appropriate Questions in Conversational Recommendation](https://mlsanity.com/paper/2105.04774/) (2021)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [Measuring "Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation](https://mlsanity.com/paper/2202.06466/) (2022)
- [AlignGroup: Learning and Aligning Group Consensus with Member Preferences for Group Recommendation](https://mlsanity.com/paper/2409.02580/) (2024)
- [Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training](https://mlsanity.com/paper/2305.07633/) (2023)
- [From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems](https://mlsanity.com/paper/2110.14844/) (2021)
- [Semantic-aware Node Synthesis for Imbalanced Heterogeneous Information Networks](https://mlsanity.com/paper/2302.14061/) (2023)
- [Comparative Explanations of Recommendations](https://mlsanity.com/paper/2111.00670/) (2021)
- [Hate Speech Detection with Generalizable Target-aware Fairness](https://mlsanity.com/paper/2406.00046/) (2024)
- [Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learning](https://mlsanity.com/paper/2411.17063/) (2024)
- [Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modelling](https://mlsanity.com/paper/2104.01716/) (2021)
- ["Are you sure?": Preliminary Insights from Scaling Product Comparisons to Multiple Shops](https://mlsanity.com/paper/2107.03256/) (2021)
- ["Does it come in black?" CLIP-like models are zero-shot recommenders](https://mlsanity.com/paper/2204.02473/) (2022)
- [A Transformer-Based Substitute Recommendation Model Incorporating Weakly Supervised Customer Behavior Data](https://mlsanity.com/paper/2211.02533/) (2022)
- [DREAM: Adaptive Reinforcement Learning based on Attention Mechanism for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2304.03984/) (2023)
- [User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations](https://mlsanity.com/paper/2308.00894/) (2023)
- [AgentDR: Dynamic Recommendation with Implicit Item-Item Relations via LLM-based Agents](https://mlsanity.com/paper/2510.05598/) (2025)
- [Attribute-aware Explainable Complementary Clothing Recommendation](https://mlsanity.com/paper/2107.01655/) (2021)
- [Personalized On-Device E-health Analytics with Decentralized Block Coordinate Descent](https://mlsanity.com/paper/2112.09341/) (2021)
- [Collaborative Reflection-Augmented Autoencoder Network for Recommender Systems](https://mlsanity.com/paper/2201.03158/) (2022)
- [Self-supervised Graph-based Point-of-interest Recommendation](https://mlsanity.com/paper/2210.12506/) (2022)
- [Enhancing ID-based Recommendation with Large Language Models](https://mlsanity.com/paper/2411.02041/) (2024)
- [Multi-modal Relational Item Representation Learning for Inferring Substitutable and Complementary Items](https://mlsanity.com/paper/2507.22268/) (2025)
- [Explainable Recommender with Geometric Information Bottleneck](https://mlsanity.com/paper/2305.05331/) (2023)
- [A Survey of Reasoning for Substitution Relationships: Definitions, Methods, and Directions](https://mlsanity.com/paper/2404.08687/) (2024)
- [Stability of Explainable Recommendation](https://mlsanity.com/paper/2405.01849/) (2024)
- [Robust Explainable Recommendation](https://mlsanity.com/paper/2405.01855/) (2024)

## Abstract (arXiv)

As a fundamental yet significant process in personalized recommendation, candidate generation and suggestion effectively help users spot the most suitable items for them. Consequently, identifying substitutable items that are interchangeable opens up new opportunities to refine the quality of generated candidates. When a user is browsing a specific type of product (e.g., a laptop) to buy, the accurate recommendation of substitutes (e.g., better equipped laptops) can offer the user more suitable options to choose from, thus substantially increasing the chance of a successful purchase. However, existing methods merely treat this problem as mining pairwise item relationships without the consideration of users' personal preferences. Moreover, the substitutable relationships are implicitly identified through the learned latent representations of items, leading to uninterpretable recommendation results. In this paper, we propose attribute-aware collaborative filtering (A2CF) to perform substitute recommendation by addressing issues from both personalization and interpretability perspectives. Instead of directly modelling user-item interactions, we extract explicit and polarized item attributes from user reviews with sentiment analysis, whereafter the representations of attributes, users, and items are simultaneously learned. Then, by treating attributes as the bridge between users and items, we can thoroughly model the user-item preferences (i.e., personalization) and item-item relationships (i.e., substitution) for recommendation. In addition, A2CF is capable of generating intuitive interpretations by analyzing which attributes a user currently cares the most and comparing the recommended substitutes with her/his currently browsed items at an attribute level. The recommendation effectiveness and interpretation quality of A2CF are demonstrated via extensive experiments on three real datasets.