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# System prepended metadata

title: Research topic ?

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# 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. 