Learning latent perception graphs for personalized unknowns recommendation

The fast-growing online-learning platforms, which are very convenient and contain rich course resources, have attracted many users to explore new knowledge online. However, the learning quality of online-learning is generally not as effective as offline classes. In offline studies in classrooms, teachers can interact with students and teach students in accordance with personal aptitude from students' feedback in classes. Without such real-time interaction, it is difficult for users to be aware of personal unknowns. In this paper, we consider an important issue to discover 'user unknowns' from the question-giving process in online-learning platforms. A novel personalized learning framework, called PagBay, is devised to recommend user unknowns in the iterative round-by-round strategy, which contributes to applications such as a conversational bot. The flow enables users to progressively discover their weakness and to help them progress. However, discovering personal unknowns is quite challenging in online-learning platforms. Even though solving the problem with previous recommender algorithms provides solutions, they often lead to suboptimal results for unknowns recommendation as they simply rely on the user ratings and contextual features of questions. Generally, questions are associated with perceptions, and mining the relationships among users, questions, and perceptions potentially provide the clue to the better unknowns recommendation. Therefore, in this paper, we develop a novel recommender framework by borrowing strengths from perception-aware graph embedding for learning user unknowns. Our experimental studies on real data show that the proposed framework can effectively discover user unknowns in online learning services.