## 3 Materials and Methods  
We conducted a systematic literature review, adopting a well-defined plan and criteria for search and exclusion. Systematic literature reviews (SLRs) are a type of secondary empirical study that aims to offer a structured overview of a particular research field. The methodology followed in an SLR is well-defined, which helps to minimize the possibility of biased literature results, although it does not eliminate the likelihood of publication bias in the primary studies. The current SLR adhered to the steps outlined in [16], which involved three primary phases: namely, planning the SLR, executing the SLR, and reporting the SLR. This section details the planning of the study, including the research questions, data sources, search strategy, and the classification and evaluation criteria. The objective of this SLR iwa to investigate the present state of AI-based techniques in recommender systems for e-commerce and identify potential gaps and opportunities for future work through a review of existing research. The main research question that guided this study is:  
_What are the most commonly used AI-based techniques in recommender systems for e-commerce, and how effective are these systems in the context of e-commerce?_  
To gain specific insights into the relevant aspects of AI-based techniques in recommender systems for e-commerce and their effectiveness, this research posed a range of related questions. These included inquiries into the bibliometric key facts of the articles, the research type of each article, the specific characteristics of AI-based techniques, and their application scenarios. The study also examined the type of research carried out thus far (theoretical, proposal, empirical), along with the type of research forums where these works have been published and presented. The precise research questions addressed by this SLR are presented in Table 1.  
Figure 1: Co-occurrences of “recommender systems” in a list of 2000 articles written on the subject of e-commerce in the last 5 years (eWOS Viewer). To gather academic publications, we utilized several databases, including ACM Digital Library, IEEE explore, ISI Web of Science, ScienceDirect, and Springer. To expand our search to non-academic publications, we utilized Google Scholar, which allowed us to access a wide range of sources, including industry/professional conferences, workshops, online journals/magazines, and corporate blogs. To ensure that our search was focused and efficient, we applied specific criteria to our data sources, which helped us to avoid assessing hundreds of thousands of articles. The time frame selected was 2011-2021, including the entire year of 2021. In this section, we provide a detailed explanation of the keyword selection process used in our systematic literature review (SLR) to ensure a comprehensive and representative sample of the relevant literature. The keyword selection was based on a thorough examination of the literature and our research objectives, which focus on artificial intelligence (AI), recommender systems (RS), electronic commerce (e-commerce), and consumption behavior. The selection of keywords was guided by the following principles:  
1. Relevance to core concepts: We aimed to identify keywords that represent the central concepts of our research topic. For instance, we used \"artificial intelligence\" and \"machine learning\" as synonyms, as machine learning is a subset of AI and frequently employed in RS literature. Similarly, we included both \"recommender systems\" and \"recommendation systems\" since they are interchangeable terms used in the field. We also used \"electronic commerce\" to represent the broader domain of e-commerce. 2. Inclusivity: We sought to ensure that our search would include articles discussing the use and impact of AI and RS in e-commerce.