Evaluation of Deep Learning and Machine Learning Models for Recommender Systems Across Various Datasets

The recommendation system is one of the most essential information services in today's online business applications, such as Amazon, Flipkart, and YouTube. In recent days, deep learning and machine learning models have performed exceedingly well in various applications related to text processing, image processing, audio and video processing. This work aims to review recent studies that evaluated behavior of various deep learning and machine learning models in recommender systems, and summarize the various key insights related to their performance in these applications. Specifically, we focus on analysis of four types of deep learning and machine learning techniques: graph-based baselines, sequential baselines, self-supervised sequential models, and self-supervised graph-based models. Moreover, these models are evaluated on four different types of datasets: Yelp 2018, Ml-1M, Amazon Beauty, and iFashion. Among the eleven different models employed for this analysis, the two self-supervised sequential models, CL4SRec and BERT4Rec, outperform in terms of two of the four distinct metrics (Recall and NDCG) used.