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.
Report on EARS'18: 1st International Workshop on ExplainAble Recommendation and Search
This is a report on the first edition of the International Workshop on ExplainAble Recommendation and Search (EARS 2018), co-located with the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018) held in Ann Arbor, Michigan, USA on July 8-12, 2018. The workshop was held on July 12, 2018, co-chaired by Yongfeng Zhang, Yi Zhang, and Min Zhang, with invited keynote speeches delivered by Prof. Paul Resnick from University of Michigan, and Dr. Qingsong Hua from Alibaba Inc. An invited panelist committee including Professors Matthew Lease, Paul Resnick, Mark Sanderson, Wlodek Zadrozny and Dr. Qingsong Hua made fruitful discussions about the past, present, and future on the research of explainable recommendation and search. The workshop accepted six contributed papers, and attracted over 80 registered participants. The scope of the workshop spans from technical approaches to explainable recommendation and search, to policy debates on the "principle of transparency" and the "right to explanation" of algorithmic decisions implied in recent EU General Data Protection Regulation (GDPR) and other similar regulations such as The California Consumer Privacy Act of 2018.
Posthoc Interpretability of Learning to Rank Models using Secondary Training Data
Predictive models are omnipresent in automated and assisted decision making scenarios. But for the most part they are used as black boxes which output a prediction without understanding partially or even completely how different features influence the model prediction avoiding algorithmic transparency. Rankings are ordering over items encoding implicit comparisons typically learned using a family of features using learning-to-rank models. In this paper we focus on how best we can understand the decisions made by a ranker in a post-hoc model agnostic manner. We operate on the notion of interpretability based on explainability of rankings over an interpretable feature space. Furthermore we train a tree based model (inherently interpretable) using labels from the ranker, called secondary training data to provide explanations. Consequently, we attempt to study how well does a subset of features, potentially interpretable, explain the full model under different training sizes and algorithms. We do experiments on the learning to rank datasets with 30k queries and report results that serve show in certain settings we can learn a faithful interpretable ranker.