User-based and item-based collaborative filtering techniques are among most explored strategies of making products’ recommendations to Users on online shopping platforms. However, a notable weakness of the collaborative filtering techniques is the cold start problem. Which include cold user problem, cold item problem and cold system problem – i.e., the failure of collaborative filtering to make recommendation of products to a new user, failure of an item to be recommended, or combination of the two respectively. Literature investigation has shown that cold user problem could be effectively addressed using technique of personalized questionnaire. Unfortunately, where the products’ database is too large (as in Amazon.com), results obtained from personalized questionnaire technique could contain some user preference uncertainties. This paper presents technique of improving personalized questionnaire with uncertainty reduction technique. In addition, the paper presents classification of product recommendation systems. In this work we will be limited to user-based cold start. Experimentation was conducted using Movielens dataset, where the proposed technique achieved significant performance improvement over personalized questionnaire technique with RMSE, Precision, Recall,1 and NDCG of 0.200, 0.227, 0.261, 0.174 and 0.249
Eye-Tracking-Based Classification of Information Search Behavior Using Machine Learning: Evidence from Experiments in Physical Shops and Virtual Reality Shopping Environments