Recommender systems have been successfully used in electronic commerce applications such as recommending books, movies, restaurants and airlines based on users’ past behaviour. More recently, such systems have made inroads into social media, for examples to recommend partners in online dating sites. In our work, we have developed a social behaviour based recommender system within an online comm...

The information overflow of today’s information society can be overcome by the usage of recommender systems. Due to the fact that most recommender systems act as black boxes, trust in a system decrease, especially when a recommendation failed. Recommender systems usually don’t offer any insight into the systems logic and cannot be questioned as it is normal for a recommendation process between ...

Complex assortments of products and services offered by online selling platforms require the provision of sales support systems assisting customers in the product selection process. Knowledge-based recommenders are intelligent sales assistance systems which guide online customers through personalized sales dialogs and automatically determine products which conform to their needs and wishes. Suc...

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete s...

As a result of the pandemic Covid19 and enforcement Movement Control Order (MCO) in Malaysia, it is difficult for Ministry Education to explain selection courses at university. Current systems such as "Selangkah ke UiTM," "eSemak Politeknik" "EduAdvisor" concentrate only on SPM graduates. result, this study proposes recommendation system that can provide recommendations course suit students' pe...

With the incorporation of new technologies, digital signages can adopt their content in real time to the audience demographic and temporal features. This research proposes an adaptive advertisement recommender system for digital signage. Our objective is to create a quantitative method for targeted advertising. After analyzing digital signage advertisement viewing data collected over the course...

We report the ‘Recurrent Deterioration’ (RD) phenomenon observed in online recommender systems. The RD phenomenon is reflected by the trend of performance degradation when the recommendation model is always trained based on users’ feedbacks of the previous recommendations. There are several reasons for the recommender systems to encounter the RD phenomenon, including the lack of negative traini...

While various recommender approaches are increasingly considered in e-learning, lack of studies of actual use is hindering the development. For several years, we have used non-algorithmic recommender features on an undergraduate course website to help students find pertinent study materials. As students earn credit from adding and evaluating materials, some have chosen to evaluate materials dis...

The current industry standard for recommender system uses variants of collaborative filtering (CF), where recipient-source relationships are determined by the extent to which the recipient and source share interests. This research attempts to improve the performance of these CF recommender systems by identifying additional measures of relationship indicators based on theories from communication...