@conference{189, author = {Joshua Dzitiro and Edgar Jembere and Anban Pillay}, title = {A DeepQA Based Real-Time Document Recommender System}, abstract = {Recommending relevant documents to users in real- time as they compose their own documents differs from the traditional task of recommending products to users. Variation in the users’ interests as they work on their documents can undermine the effectiveness of classical recommender system techniques that depend heavily on off-line data. This necessitates the use of real-time data gathered as the user is composing a document to determine which documents the user will most likely be interested in. Classical methodologies for evaluating recommender systems are not appropriate for this problem. This paper proposed a methodology for evaluating real-time document recommender system solutions. The proposed method- ology was then used to show that a solution that anticipates a user’s interest and makes only high confidence recommendations performs better than a classical content-based filtering solution. The results obtained using the proposed methodology confirmed that there is a need for a new breed of recommender systems algorithms for real-time document recommender systems that can anticipate the user’s interest and make only high confidence recommendations.}, year = {2018}, journal = {Southern Africa Telecommunication Networks and Applications Conference (SATNAC) 2018}, chapter = {304-309}, month = {02/09-05/09}, publisher = {SATNAC}, address = {South Africa}, }