The vast amount of online products data such as product properties, or product reviews plays an essential role in providing better information to the consumers to make a purchase decision. Thus, product ranking is a valuable research topic while many methods proposed by researchers in different approaches and case studies. This paper aims to develop a Systematic Literature Review (SLR) to summarise existing research and finding new gaps in product ranking research. We develop SLR by defining inclusion criteria, initiating preliminary findings, selecting primary studies and summarizing the outcome of results. We proposed three dimensions as research questions. It consists of ranking item types of product ranking, approaches of product ranking and dataset characteristics of each study. First, we found three ranking item types of product ranking that indicate what will be rank in the studies. It consists of product ranking, aspect ranking, and review ranking. Second, there are four approaches, namely: collaborative filtering, content-based recommendation, hybrid-based and knowledge-based. Third, datasets characteristics summarise the information of datasets like the type of data and statistics. Also, we found new gaps by identifying each dimension to positioning for further research in the future.