Semantics-aware Post-hoc Explainability on Social Recommender Systems

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One of the fundamental trade-offs in social recommender systems is that of accuracy and explainability. To mitigate this trade-off, the objective of this research program is to develop post-hoc explanation models that would benefit from the structured and semantic knowledge represented in knowledge graphs, such as DBpedia, Freebase or YAGO, to systematically enhance the explainability of recommender systems on social media, whist maintaining their predictive accuracy. A post-hoc explanation model justifies the recommendations previously given to a user by a recommender system. A knowledge graph-based post-hoc explanation model would systematically investigate how to employ domain knowledge of a recommender system to increase the faithfulness of the explanation model in approximating the recommendations. There are three main novel aspects of this research: (1) we will develop techniques for pruning knowledge graphs such that items in the recommender system are linked to relevant knowledge graph subgraphs in order to efficiently and effectively generate post-hoc explanations for the recommended items to the users; (2) we will develop mechanisms for generating personalized post-hoc explanations for users based on their daily activities on social media integrated with the knowledge graph subgraphs; (3) we will also explore different techniques for enriching the explanations via linking them to knowledge graphs, improving the human-understandability of the generated post-hoc explanations. The primary outcome of this research program will be development of the next generation of post-hoc explanation models that can be adopted by the growing social media analytics sector to effectively respond to the natural demand of human-beings for understanding why a model has made a specific decision. The outcomes can be utilized by domain specific social recommender systems, such as job recommendation, music recommendation and online marketing solutions, to improve the users' acceptance or satisfaction of recommendations and increase trust, which consequently, sets the stage for the sustainability of social media. The proposed research program will also provide a solid foundation for the training of highly qualified personnel including 3 PhD and 6 MASc students who will gain expertise in the fields of information retrieval, social media mining, and semantic web technologies. It is expected that these highly qualified personnel are able to be immediately absorbed in the lively and growing data science and analytics industry in Canada.