What are the current gaps and limitations of explainable recommender systems research and practice?

1 Definition and types of explanations

An explanation is a piece of information that clarifies or justifies the recommendation to the user. Explanations can have different goals, such as transparency, persuasiveness, effectiveness, or satisfaction. Explanations can also have different forms, such as textual, visual, or interactive. Depending on the type of explanation, the system may need to consider different factors, such as the user's preferences, context, cognitive abilities, and emotions.

2 Challenges of generating explanations

One of the main challenges of generating explanations is to balance the trade-off between simplicity and complexity. On one hand, explanations should be simple and concise enough to be understandable and useful for the user. On the other hand, explanations should be complex and detailed enough to reflect the true logic and rationale of the system. Moreover, explanations should be consistent and coherent across different recommendations and scenarios, as well as aligned with the user's expectations and needs.

3 Challenges of evaluating explanations

Another major challenge of XRS is to evaluate the quality and impact of explanations on the user and the system. Evaluating explanations is not straightforward, as it involves multiple criteria, such as accuracy, relevance, completeness, persuasiveness, satisfaction, trust, and behavior. Moreover, evaluating explanations requires gathering user feedback, which can be costly, time-consuming, and biased. Furthermore, evaluating explanations may depend on the context, the domain, and the user group, which can vary widely and dynamically.

4 Limitations of existing methods and models

A third limitation of XRS is that the existing methods and models for generating and evaluating explanations are often insufficient or inadequate for the complex and diverse scenarios of real-world applications. For example, many methods and models rely on predefined rules or templates, which may not capture the nuances and subtleties of natural language and human reasoning. Moreover, many methods and models are based on shallow or black-box approaches, which may not provide enough transparency or accountability for the system's decisions.

5 Ethical and social implications of explanations

A fourth limitation of XRS is that they may raise ethical and social issues that need to be addressed carefully and responsibly. For example, explanations may influence the user's choices and actions, which may have positive or negative consequences for the user and others. Moreover, explanations may reveal sensitive or personal information about the user or the system, which may affect the user's privacy and security. Furthermore, explanations may reflect or reinforce biases or stereotypes, which may harm the user's fairness and diversity.

6 Future directions and opportunities of XRS

Despite the gaps and limitations of XRS, they also offer many opportunities and directions for future research and practice. For example, XRS can leverage advanced techniques and tools, such as natural language generation, deep learning, or interactive visualization, to create more natural, flexible, and adaptive explanations. Moreover, XRS can explore new domains and applications, such as health, education, or entertainment, to provide more meaningful and beneficial recommendations. Furthermore, XRS can foster interdisciplinary collaboration and user involvement, to design more ethical and user-centric systems.