Evaluation of context awareness algorithms for recommending mobile content - Dong, Hong Liang (2014)
Context-aware recommender systems (CARSs) gradually play a crucial role in modern information systems. Previous studies showed that they had great influence to users’ behaviors of information retrieval and decision making process. With the fast growth of mobile systems (e.g., smart phones), context-aware recommendation on mobile content became a hot trend in the academic studies and industrial applications. These systems are also known as mobile context-aware recommender systems (MCARSs). The various approaches in context-aware recommender systems were categorized into three approaches: pre-filtering, post-filtering, and contextual modelling. There are numbers of algorithms in each approach. These approaches and algorithms extended their usage from web based to mobile context-aware recommender systems. However, most of previous studies focused on the web based context-aware recommender systems, and there were few evaluations on the context-awareness recommendation algorithms for mobile content. The richness and dynamic of contextual information on mobile devices makes mobile context-aware recommender systems specific to web based ones. This study purposes to fill the gap of evaluating context-awareness algorithms for recommending mobile content.