What are the main challenges and trade-offs of designing explainable recommender systems?

2 What are the main types and methods of explanation?

Depending on the purpose and audience of the explanation, recommender systems can use various types and methods of explanation. User-based explanations show how the recommendation is influenced by the user's profile, behavior, or feedback, while item-based explanations reveal how the recommendation is affected by the item's features, attributes, or ratings. System-based explanations demonstrate how the recommendation is determined by the system's model, algorithm, or parameters. Social-based explanations indicate how the recommendation is influenced by the social context, such as other users, friends, or experts. Common methods include rule-based explanations using simple and explicit rules to generate recommendations and explanations; feature-based explanations using the features or attributes of the items; model-based explanations utilizing the internal representation or logic of the system's model; and case-based explanations using previous or similar cases or examples.

3 What are the main challenges of explainable recommender systems?

Designing explainable recommender systems is no easy feat, as it involves various challenges and trade-offs that need to be weighed carefully. Complexity is a major issue, as recommender systems frequently use complex and opaque models or algorithms that are difficult to interpret or explain. Diversity is also a factor, as different types of users, items, contexts, and goals may require different levels of detail, format, and style when it comes to explanations. Additionally, it is important to evaluate the effectiveness and impact of the explanations based on criteria such as accuracy, relevance, persuasiveness, satisfaction, and trust. Finally, recommender systems must remain ethical by avoiding misleading, biased or harmful explanations that respect user privacy, autonomy, and dignity.

4 What are the main trade-offs of explainable recommender systems?

Designing explainable recommender systems involves making trade-offs between accuracy and explainability, simplicity and completeness, consistency and diversity, as well as utility and satisfaction. For example, a recommender system may have to sacrifice some accuracy or performance in order to provide more transparent recommendations. Or it may have to simplify or omit some information in order to provide more concise explanations. Additionally, the system may have to provide consistent or coherent explanations that match the user's expectations or diverse or novel explanations that challenge or surprise the user. Finally, it may have to provide useful or relevant explanations that help the user make a decision or take an action, or provide satisfying or enjoyable explanations that entertain or engage the user.

5 What are some possible solutions and best practices for explainable recommender systems?

Despite the challenges and trade-offs, there are some solutions and best practices that can help recommender systems to provide better explanations. For instance, hybrid models or methods combining different types of explanation can be used to achieve more comprehensive and diverse explanations. Adaptive techniques that adjust the content, format, or style of the explanation according to the user's profile, preferences, behavior, feedback, or context can be used to create more personalized and relevant explanations. Additionally, interactive features that allow the user to control, modify, or query the explanations with filters, sliders, buttons, or questions can produce more flexible and engaging explanations. Finally, ethical principles and guidelines such as accuracy, transparency, accountability, and privacy should be employed to ensure the quality, fairness, and responsibility of the explanations for more trustworthy and respectful explanations.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?