In our modern day and age, students with different abilities tend to undertake professional examinations (PEs) to obtain certifications that would prove their knowledge and expertise in their respective fields. This will help them when seeking a career boost in various domains. However, a challenging point arises in which many students express a lack of awareness about which PEs to consider, and which PE is best suited to their professional needs. In this research, a solution is proposed to overcome this challenge by designing and developing a web-based recommendation system based on a textual Conversational Agent (CA) called the Conversational Agent for Professional Examinations (CAPEs) Advisory. The CAPEs Advisory provides smart recommendations for better exam pathways that would suit the student’s various types of knowledge and skill level at University. The proposed architecture for the CAPEs Advisory uses Natural Language Processing (NLP) techniques by applying both Pattern Matching (PM) and a semantic similarity algorithm to extract keywords from the user’s utterances to match patterns in the scripted conversation. An evaluation methodology and experiments have been designed and conducted by using subjective and objective methods to evaluate the CAPEs Advisory components. The results showed a statistically significant impact on the effectiveness of the CAPEs Advisory engine in recognizing 97.36% of the utterances. In addition, the results show that the CAPEs Advisory is effective as a Professional Examinations Advisory with the majority user satisfaction being 83.3%.
The goal of ecommerce recommender systems is to increase sales by providing accurate recommendations to the users. However, users purchase intention plays a vital role in making a purchase and ignoring their desire to make a purchase before providing a recommendation can negatively influence recommendation accuracy because it does not target the specific users. The proposed model identifies eight factors that can influence a user’s purchase intention and categorises them as user and population behaviors. These factors are fed into the AdaBoost algorithm to predict a user’s purchase intention. The results show that we achieve high accuracy and the ROC area is 0.91, when we use the identified factors to predict a user’s purchase intention. The proposed model outperforms two state-of-the-art models that have been used to predict a user’s purchase intention.
Algawiaz, Danah, Dobbie, Gillian, and Alam, Shafiq
2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) Intelligent Systems and Knowledge Engineering (ISKE), 2019 IEEE 14th International Conference on. :324-328 Nov, 2019