Explainable Recommendation: A Survey and New Perspectives (1804.11192v10)

Abstract: Explainable recommendation attempts to develop models that generate not only high-quality recommendations but also intuitive explanations. The explanations may either be post-hoc or directly come from an explainable model (also called interpretable or transparent model in some contexts). Explainable recommendation tries to address the problem of why: by providing explanations to users or system designers, it helps humans to understand why certain items are recommended by the algorithm, where the human can either be users or system designers. Explainable recommendation helps to improve the transparency, persuasiveness, effectiveness, trustworthiness, and satisfaction of recommendation systems. It also facilitates system designers for better system debugging. In recent years, a large number of explainable recommendation approaches -- especially model-based methods -- have been proposed and applied in real-world systems. In this survey, we provide a comprehensive review for the explainable recommendation research. We first highlight the position of explainable recommendation in recommender system research by categorizing recommendation problems into the 5W, i.e., what, when, who, where, and why. We then conduct a comprehensive survey of explainable recommendation on three perspectives: 1) We provide a chronological research timeline of explainable recommendation. 2) We provide a two-dimensional taxonomy to classify existing explainable recommendation research. 3) We summarize how explainable recommendation applies to different recommendation tasks. We also devote a chapter to discuss the explanation perspectives in broader IR and AI/ML research. We end the survey by discussing potential future directions to promote the explainable recommendation research area and beyond.

Analyzing "Explainable Recommendation: A Survey and New Perspectives" by Yongfeng Zhang and Xu Chen

The paper "Explainable Recommendation: A Survey and New Perspectives" by Yongfeng Zhang and Xu Chen offers a comprehensive exploration of explainable recommendation systems, an emergent area of interest that intertwines recommendation algorithms with the capability to provide intuitive explanations. The document methodically reviews various dimensions of explainable recommendation, offering a structured taxonomy of methods, distinct explanation strategies, evaluation protocols, and potential applications.

Core Contributions

The authors introduce the concept of explainable recommendation by drawing a distinction between traditional recommendation systems that focus purely on predicting user preferences and systems that also address the "why" behind each recommendation. This dual perspective aims to enhance user trust, satisfaction, and system transparency, ultimately improving the overall efficacy of the recommendation process.

Taxonomy of Explainable Recommendation Research

One of the primary contributions of this paper is a dual-dimensional taxonomy to classify existing research in the domain of explainable recommendation. The authors distinguish between two main dimensions:

1. Information Source (or Display Style):
   • Relevant User or Item Explanation: This method relies on item-based or user-based collaborative filtering, offering recommendations based on similar users or items.
   • Feature-based Explanation: Recommend items based on the matching of user profiles with item features.
   • Opinion-based Explanation: Utilize user-generated text contributions like reviews to provide aspect or sentence-level explanations.
   • Sentence Explanation: Provide explanations either through template-based sentences or more advanced natural language generation techniques.
   • Visual Explanation: Highlight specific regions of item images that are of interest to the user, leveraging techniques like neural attention mechanisms.
   • Social Explanation: Utilize social connections and activities as explanatory tools.
2. Algorithmic Mechanism:
   • Factorization Models: Incorporate explicit factor models, attention-driven models, or tensor factorization for generating explainable recommendations.
   • Topic Modeling: Use LDA-based approaches to harness review text for explainable topic-wise recommendations.
   • Graph-based Models: Employ tripartite graphs or overlapping co-clusters to identify influential user-item interactions.
   • Deep Learning: Leverage neural networks, including CNNs and RNNs, to emphasize important review content or generate natural language explanations.
   • Knowledge Graph-based Models: Integrate knowledge graph reasoning to support explainable recommendations.
   • Rule Mining: Utilize mining techniques like association rule mining to derive straightforward explanations.
   • Post-Hoc/Model-Agnostic Methods: Generate explanations independently from the underlying recommendation model, ensuring flexibility.

Numerical Results and Bold Claims

The paper doesn't emphasize specific numerical results, opting instead for a broad survey of methodologies and their qualitative implications. However, it boldly asserts that these explainable recommendations can significantly enhance user trust and system effectiveness. For example, the section detailing explicit factor models (EFM) showcases how alignments between latent dimensions and explicit features yield improved transparency and potentially higher user satisfaction.

Implications and Future Directions

The implications of this research are manifold, influencing both practical implementations and theoretical advancements. From a practical standpoint, integrating explainable recommendation systems can make recommendation engines more user-friendly and trustworthy. This aligns with broader trends in AI towards transparent and interpretable systems, particularly in sensitive domains like healthcare and finance.

From a theoretical perspective, the survey highlights critical areas for future exploration:

1. Explainable Deep Learning for Recommendation: While progress has been made in this field, the challenge remains to design inherently interpretable deep learning models.
2. Knowledge-enhanced Explainable Recommendation: Combining domain-specific knowledge graphs with recommendation algorithms can provide more accurate and human-like explanations.
3. Multi-Modality and Heterogeneous Information Modeling: Leveraging diverse data sources like text, images, and user context to improve both recommendation quality and explainability.
4. Context-aware Explanations: Dynamic user preferences necessitate contextual explanations that evolve over time.
5. Evaluation Metrics: Developing robust offline and online evaluation protocols for measuring the quality and effectiveness of explanations.

Conclusion

"Explainable Recommendation: A Survey and New Perspectives" serves as a definitive reference, mapping the landscape of explainable recommendation systems. The authors provide a meticulously organized overview of the state-of-the-art, segmented by information sources and algorithm mechanisms. Their work underscores the necessity of integrating explainability into recommendation systems to foster trust and efficiency, laying the groundwork for future advancements in this pivotal area.