Most of the existing recommender systems understand the preference level of users based on user-item interaction ratings. Rating-based recommendation systems mostly ignore negative users/reviewers (who give poor ratings). There are two types of negative users. Some negative users give negative or poor ratings randomly, and some negative users give ratings according to the quality of items. Some negative users, who give ratings according to the quality of items, are known as reliable negative users, and they are crucial for a better recommendation. Similar characteristics are also applicable to positive users. From a poor reflection of a user to a specific item, the existing recommender systems presume that this item is not in the user’s preferred category. That may not always be correct. We should investigate whether the item is not in the user’s preferred category, whether the user is dissatisfied with the quality of a favorite item or whether the user gives ratings randomly/casually. To overcome this problem, we propose a Social Promoter Score (SPS)-based recommendation. We construct two user-item interaction matrices with users’ explicit SPS value and users’ view activities as implicit feedback. With these matrices as inputs, our attention layer-based deep neural model deepCF_SPS learns a common low-dimensional space to present the features of users and items and understands the way users rate items. Extensive experiments on online review datasets present that our method can be remarkably futuristic compared to some popular baselines. The empirical evidence from the experimental results shows that our model is the best in terms of scalability and runtime over the baselines.
