RETRACTED ARTICLE: Optimized machine learning based collaborative filtering (OMLCF) recommendation system in e-commerce

Abstract

A recommender system (RS) is a subcategory of an information filtering system that attempts the prediction of the score or the importance given to an item by a user. RS has garnered the attention of the business community and individuals towards itself owing to its significance in the e-commerce field. One of the most common methods of the RS used for the generation of recommendations is the CF technique (collaborative filtering). But, CF-based RS yields untrustworthy similarity information and yields a recommendation quality that is not satisfactory. Support vector machine (SVM) helps in enhancing issues in the CF technique. The parameter of the SVM algorithm minimizes the system's accuracy, and therefore in classifier improved ant colony optimization (IACO) is brought-in for parameter optimization. In the newly introduced system, RS will be carried out in two stages which include (1) SVM classifier for classifying the entities into positive and negative feedback. The best value achieved indicates the optimized values of the parameters of SVM employing the IACO algorithm, which are given in the form of an input to the classifier to carry out pair-wise classification, (2) then, we construct SVM–IACO based collaborative filtering algorithm. The collaborative filtering recommendation's execution is only done on the entities' positive-feedback. The actual content used for recommendation is highly reduced owing to the classification much earlier; therefore the collaborative filtering improves the efficiency in comparison with the classical one. Tests on Taobao data (an Alibaba owned Chinese online shopping website) revealed that the algorithm yields a superior recommendation accuracy thereby commanding a particular predominant place in the e-commerce field.