CAAM- CNN With Autoencoder Attention Mechanism for Recommendation System to Improve Trust in E-commerce Industry

COVID-19 crisis expedited the spread of e-commerce, moving beyond luxury goods and services and towards new businesses, product categories and basic necessities that are important to many people. Customers can have access to a wide range of products from the comfort and security of their homes but still have trusting issues while purchasing online. Online feedback either comments or ratings given by customers, has the power to affect how consumers make purchasing decisions. This meteoric expansion of digitized information and number of users over internet brought the use of recommender systems as a significant solution by offering more proactive and individualized information services and enable users to quickly narrow their choices and make wise decisions.However, the majority of existing recommendation models have poor accuracy issues as a result of data scarcity and cold start issues because recommender systems only have a limited amount of explicit data and this also lead to trust issue where users are fear of being taken advantage due to misleading information on the e-commerce websites. To solve this problem and provide quality recommendations, a new trust-based approach CNN with Autoencoder Attention Mechanism (CAAM) is proposed. CAAM uses static and dynamic trust combined with the user-item preferences and pass through the auto-encoder to reduce noise and make encoded features visible. After that convolution is used to combine encoded features with user and item features in latent space to provide nonlinear mapping and increase the reliability of features and to improve the recommendations which ultimately lead to improve the trust of people over e-commerce.The Epinions dataset is used to evaluate the concept in a number of measures.