# Research topic ? ## explainable recommender systems > Idea is to not only recommend a set of items, but also the paths in the knowledge graph to show the reason why the method has made these recommendations. [paper referenced by the survey](https://dl.acm.org/doi/abs/10.1145/3331184.3331203) [survey on explainable recommenders](https://arxiv.org/ftp/arxiv/papers/1804/1804.11192.pdf) * AI is used in many high-stakes applications * medicine / health * giving out loans * GDPR - right to explanation for automatic decision making algorithms * most effective algorithms are hardest to explain. * Model as black box * Desired explainability * combine logic-based reasoning together with machine learning * Expert systems 1. Explainable Deep Learning for Recommendation 2. Knowledge-enhanced Explainable Recommendation 3. Multi-Modality and Heterogenous Information Modeling * using heterogenous data not just for recommendation but for explanations as well 4. Context-aware Explanations * just like how the recommendations themselves are context aware (for example using time and location), explanations could be context aware as well. * current explainable recommendation models are static (users are profiled on a training set and explanations are generated based on that) * It could be based on exploration as well 5. Aggregation of Different Explanations * Different explainable models for different explanations * Challange: select best combinations of explanations for a recommendation * Integrate statistical and logical reasoning approaches into machine learning 6. Explainable Recommendation as Reasoning * ## current top-k recommenders ## offline policy evaluation techniques for top k recommenders ## A Survey on Reinforcement Learning for Recommender Systems - open issues Yuanguo Lin, et al. ### Sampling efficiency Need to improve sampling efficiency as there is not enough user feedback.