A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of MOOC
Zhuoxuan Jiang, Shanshan Feng, Gao Cong, Chunyan Miao, Xiaoming Li · 2017
Recent years have witnessed the proliferation of Massive Open Online Courses (MOOCs).With massive learners being offered MOOCs, there is a demand that the forum contents within MOOCs need to be classified in order to facilitate both learners and instructors.Therefore we investigate a significant application, which is to associate forum threads to subtitles of video clips.This task can be regarded as a document ranking problem, and the key is how to learn a distinguishable text representation from word sequences and learners' behavior sequences.In this paper, we propose a novel cascade model, which can capture both the latent semantics and latent similarity by modeling MOOC data.Experimental results on two real-world datasets demonstrate that our textual representation outperforms state-of-the-art unsupervised counterparts for the application.