Abstract
Digital publishing resources contain a lot of useful and authoritative knowledge. It may be necessary to reorganize the resources by concepts and recommend the related concepts for e-learning. A recommender system is presented in this paper based on the semantic relatedness of concepts computed by texts from digital publishing resources. Firstly, concepts are extracted from encyclopedias. Information in digital publishing resources is then reorganized by concepts. Secondly, concept vectors are generated by skip-gram model and semantic relatedness between concepts is measured according to the concept vectors. As a result, the related concepts and associated information can be recommended to users by the semantic relatedness for learning or reading. History data or users’ preferences data are not needed for recommendation in a specific domain. The technique may not be language-specific. The method shows potential usability for e-learning in a specific domain.
Highlights
Digital publishing resources include e-books, digital newspapers, digital magazines, digital encyclopedias, digital yearbooks and so on
A recommender system based on skip-gram model is presented in this paper without considering history data or preferences data of users when they learn the knowledge organized in concepts for a specific domain
Since the semantic relatedness in the recommender system is computed by the text of digital publishing resources which is normally written in different languages holding a relatively complete and authoritative collection of concepts and texts in a specific domain, the proposed method can be used in the different language environments and can cover almost all important concepts in a domain
Summary
Digital publishing resources include e-books, digital newspapers, digital magazines, digital encyclopedias, digital yearbooks and so on. The encyclopedia of the historical domain contains the major concepts related to history which include historical figures, historical events, and so on These concepts are mentioned in other more general texts as paragraphs or sections in e-books, digital magazines, digital newspapers, and so on. It is useful to reorganize the domain knowledge from the digital publishing resources by concepts. A recommender system based on skip-gram model is presented in this paper without considering history data or preferences data of users when they learn the knowledge organized in concepts for a specific domain.
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