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.
TEM: Tree-enhanced Embedding Model for Explainable Recommendation
While collaborative filtering is the dominant technique in personalized recommendation, it models user-item interactions only and cannot provide concrete reasons for a recommendation. Meanwhile, the rich side information affiliated with user-item interactions (e.g., user demographics and item attributes), which provide valuable evidence that why a recommendation is suitable for a user, has not been fully explored in providing explanations. On the technical side, embedding-based methods, such as Wide&Deep and neural factorization machines, provide state-of-the-art recommendation performance. However, they work like a black-box, for which the reasons underlying a prediction cannot be explicitly presented. On the other hand, tree-based methods like decision trees predict by inferring decision rules from data. While being explainable, they cannot generalize to unseen feature interactions thus fail in collaborative filtering applications. In this work, we propose a novel solution named Tree-enhanced Embedding Method that combines the strengths of embedding-based and tree-based models. We first employ a tree-based model to learn explicit decision rules (aka. cross features) from the rich side information. We next design an embedding model that can incorporate explicit cross features and generalize to unseen cross features on user ID and item ID. At the core of our embedding method is an easy-to-interpret attention network, making the recommendation process fully transparent and explainable. We conduct experiments on two datasets of tourist attraction and restaurant recommendation, demonstrating the superior performance and explainability of our solution.
CMEOC—An expert system in the coal mining industry
Abstract This paper introduces an expert system in the mining industry, called the Coal Mining Expert and Optimization Consultation System (CMEOC). The paper mainly describes the architecture of this system, including the knowledge base, inference engine, and relevant optimization techniques. The techniques include multi-objective programming, fuzzy sets, and integer programming. The application results present evidence of the usefulness of the system.