Xuemin Zhao

Hengyao Bao, Xihua Li, Xuemin Zhao et al.

Cognitive diagnosis, the goal of which is to obtain the proficiency level of students on specific knowledge concepts, is an fundamental task in smart educational systems. Previous works usually represent each student as a trainable knowledge proficiency vector, which cannot capture the relations of concepts and the basic profile(e.g. memory or comprehension) of students. In this paper, we propose a method of student representation with the exploration of the hierarchical relations of knowledge concepts and student embedding. Specifically, since the proficiency on parent knowledge concepts reflects the correlation between knowledge concepts, we get the first knowledge proficiency with a parent-child concepts projection layer. In addition, a low-dimension dense vector is adopted as the embedding of each student, and obtain the second knowledge proficiency with a full connection layer. Then, we combine the two proficiency vector above to get the final representation of students. Experiments show the effectiveness of proposed representation method.

Yi Wang, Xuemin Zhao, Zhenlong Sun et al.

Latent Dirichlet allocation (LDA) is a popular topic modeling technique in academia but less so in industry, especially in large-scale applications involving search engine and online advertising systems. A main underlying reason is that the topic models used have been too small in scale to be useful; for example, some of the largest LDA models reported in literature have up to $10^3$ topics, which cover difficultly the long-tail semantic word sets. In this paper, we show that the number of topics is a key factor that can significantly boost the utility of topic-modeling systems. In particular, we show that a "big" LDA model with at least $10^5$ topics inferred from $10^9$ search queries can achieve a significant improvement on industrial search engine and online advertising systems, both of which serving hundreds of millions of users. We develop a novel distributed system called Peacock to learn big LDA models from big data. The main features of Peacock include hierarchical distributed architecture, real-time prediction and topic de-duplication. We empirically demonstrate that the Peacock system is capable of providing significant benefits via highly scalable LDA topic models for several industrial applications.