WWW’19 Tutorial on Explainable Recommendation and Search
Explainable recommendation and search attempt to develop models or methods that not only generate high-quality recommendation or search results, but also intuitive explanations of the results for users or system designers, where the explanations can be either post-hoc or directly come from an explainable model. Explainable recommendation and search can help to improve the system transparency, persuasiveness, trustworthiness, and effectiveness. This is even more important in personalized search and recommendation scenarios, where users would like to know why a particular product, web page, news report, or friend suggestion exists in his or her own search and recommendation lists. The tutorial focuses on the research and application of explainable recommendation and search algorithms, as well as their application in real-world systems such as search engine, e-commerce and social networks. The tutorial aims at introducing and communicating explainable recommendation and search methods to the community, as well as gathering researchers and practitioners interested in this research direction for discussions, idea communications, and research promotions.
A Reinforcement Learning Framework for Explainable Recommendation
Explainable recommendation, which provides explanations about why an item is recommended, has attracted increasing attention due to its ability in helping users make better decisions and increasing users' trust in the system. Existing explainable recommendation methods either ignore the working mechanism of the recommendation model or are designed for a specific recommendation model. Moreover, it is difficult for existing methods to ensure the presentation quality of the explanations (e.g., consistency). To solve these problems, we design a reinforcement learning framework for explainable recommendation. Our framework can explain any recommendation model (model-agnostic) and can flexibly control the explanation quality based on the application scenario. To demonstrate the effectiveness of our framework, we show how it can be used for generating sentence-level explanations. Specifically, we instantiate the explanation generator in the framework with a personalized-attention-based neural network. Offline experiments demonstrate that our method can well explain both collaborative filtering methods and deep-learning-based models. Evaluation with human subjects shows that the explanations generated by our method are significantly more useful than the explanations generated by the baselines.
Pearson Correlation Coefficient
A measure of the correlation of two variables X and Y measured on the same object or organism, that is, a measure of the tendency of the variables to increase or decrease together. It is defined as the sum of the products of the standard scores of the two measures divided by the degrees of freedom.