Citta: A Lite Semantic Recommendation Framework for Digital Libraries

Abstract

Most of the recommendation and search frameworks in Digital Libraries follow a keyword-based approach to resolve text-based search queries. Keyword-based methods usually fail to capture the semantic aspects of the user’s query and often lead to a misleading set of results. In this work, we propose an efficient and content-sentiment aware semantic recommendation framework, Citta. The framework is designed with the BERT language model. It is designed to retrieve semantically related reading recommendations with short input queries and shorter response times. We test the proposed framework on the CMU Book Summary Dataset and discuss the observed advantages and shortcomings of the framework.