Explainable Recommendation: Theory and Applications

Understand

Although personalized recommendation has been investigated for decades, the wide adoption of Latent Factor Models (LFM) has made the explainability of recommendations a critical issue to both the research community and practical application of recommender systems.

• For example, in many practical systems the algorithm just provides a personalized item recommendation list to the users, without persuasive personalized explanation about why such an item is recommended while another is not.
• Unexplainable recommendations introduce negative effects to the trustworthiness of recommender systems, and thus affect the effectiveness of recommendation engines.
• In this work, we investigate explainable recommendation in aspects of data explainability, model explainability, and result explainability, and the main contributions are as follows: 1.