A Systematic Study on a Customer’s Next-Items Recommendation Techniques

Abstract

A customer’s next-items recommender system (NIRS) can be used to predict the purchase list of a customer in the next visit. The recommendations made by these systems support businesses by increasing their revenue and providing a more personalized shopping experience to customers. The main objective of this paper is to provide a systematic literature review of the domain to analyze the recent techniques and assist future research. The paper examined 90 selected studies to answer the research questions concerning the key aspects of NIRSs. To this end, the main contribution of the paper is that it provides detailed insight into the use of conventional and deep learning techniques, the popular datasets, and specialized metrics for developing and evaluating these systems. The study reveals that conventional machine learning techniques have been quite popular for developing NIRSs in the past. However, more recent works have mainly focused on deep learning techniques due to their enhanced ability to learn sequential and temporal information. Some of the challenges in developing NIRSs that need further investigation are related to cold start, data sparsity, and cross-domain recommendations.

Enhancing GPS Accuracy with Machine Learning: A Comparative Analysis of Algorithms

“…Gradient Boosting Decision Trees (GBDT) [45] is an additional regression model that includes a set of decision trees. A single decision tree has an overfitting problem, but the GBDT algorithm can overcome this by combining hundreds of weak decision trees consisting of several leaf nodes.…”