Hybrid Translation and Language Model for Micro Learning Material Recommendation

As an emerging pedagogy, micro learning aims to make use of people's fragmented spare time and provide personalized online learning service, for example, by pushing fragmented knowledge to specific learners. In the context of big data, the recommender system is the key factor for realizing the online personalization service, which significantly determines what information will be finally accessed by the target learners. In the education discipline, due to the pedagogical requirements and the domain characteristics, ranking recommended learning materials is essential for maintaining the outcome of the massive learning scenario. However, many widely used recommendation strategies in other domains showed defectiveness in the ability to rank the recommended results. In this paper, we propose a novel recommendation strategy based on the combination of the language model and the translation model. The proposed recommendation strategy aims to filter out unsuitable learning materials and ranks the recommended learning materials more effectively.