Authors: Mohamed Ramzi Haddad; Hajer Baazaoui
Addresses: Riadi Laboratory, École Nationale des Sciences de l'Informatique, University of Manouba, Campus de la Manouba, La Manouba, 2010, Tunisia ' Riadi Laboratory, École Nationale des Sciences de l'Informatique, University of Manouba, Campus de la Manouba, La Manouba, 2010, Tunisia
Abstract: Recommendation algorithms aim at predicting customers' interests and purchases using different ideas and hypotheses. Consequently, system designers need to choose the recommendation approach that is the most suitable with regard to their products' nature and consumers' behaviours within the application field. In this paper, we propose an adaptive recommendation model based on statistical modelling to assist consumers facing choice overload by predicting their interests and consumption behaviours. We also propose a dynamic variant of the model taking into account the recommendations' time-value during interactive online recommendation scenarios. Our proposal has endured a two-fold evaluation. First, we conducted an offline comparative study on the MovieLens recommendation dataset in order to assess our model's performance with regard to several widely adopted recommendation techniques. Then, the model was evaluated within a real time online news recommendation platform to highlight its adaptability, scalability and efficiency in a highly interactive application domain.
Keywords: adaptive recommendation model; interactive recommendation; continuous recommendation; consumer behaviour modelling and prediction.
International Journal of Data Mining, Modelling and Management, 2018 Vol.10 No.1, pp.89 - 111
Received: 21 Mar 2017
Accepted: 09 Jul 2017
Published online: 25 Jan 2018 *