## 5 Discussion
### AI-Based Techniques in Recommender Systems for e-commerce
#### 5.1.1. Data Science and Machine Learning Techniques Used in Developing RSs for E-Commerce  
The articles grouped in this section of our analysis pertains to studies that focus on research that employs taxonomies, ontologies, and machine learning to generate recommendations. For instance [2] employed a probabilistic topic modeling approach to uncover hidden topics, which served as the foundation for calculating item and term similarity for generating recommendations. Some other authors used unstructured reviews to construct a structured semantic representation of database items, enabling the application of semantic queries and additional machine learning analytics [20; 21; 22]. Additionally, various studies have focused on incorporating machine learning (ML) to classify and group user preferences, beyond the use of taxonomies or semantic webs. This application of ML has demonstrated the wide range of ML techniques and related technologies. Several studies utilized a range of ML techniques and related technologies to classify and cluster user preferences. The AdaBoost machine learning algorithm was used by [23]. Additionally, clustering, decision trees, and association rule mining were used by [24]. The difference factor' K-NN collaborative filtering method, known as DF-KNN, was used by [25]. Agglomerative clustering algorithms was used by [26]. Support vector machines (SVM), decision trees, and inductive logic programming were applied by [27; 28]. Non-linear kernel techniques were employed to assess individual behaviors [29]. A Gibbs sampling-based latent Dirichlet allocation classifier framework was used to classify product reviews into positive, negative, and neutral [30]. Finally, advanced distributed machine learning algorithms, such as variants of distributed Kalman filters, distributed alternating least square recommenders, and distributed mini-batch stochastic gradient descent (SGD)-based classifiers, and highly scalable distributed computation and machine learning platforms, including Apache Spark, Spark MLlib, Spark Streaming, Python/PySpark, R/SparkR, and Apache Kafka, were utilized in a high-performance, distributed, and fault-tolerant architecture by [31]. Table 7 summarizes the various machine learning techniques and related technologies used to classify and cluster user preferences in the context of recommender systems for e-commerce. These methods go beyond the use of taxonomies or semantic webs and allow for the handling of uncertainty, providing a multigranular context, and accurate classification and clustering of user preferences. The table below provides an overview of the specific algorithms used by different studies. This information can be useful for researchers and practitioners looking to incorporate machine learning into their recommender systems to improve recommendation relevance and accuracy. \begin{table}
\begin{tabular}{c c} \hline \hline
**Study** & **Techniques/Technologies Used** \\ \hline
[8] & Probabilistic topic modeling, item and term similarity calculation \\ \hline
[20; 21; 22] & Unstructured reviews, structured semantic representation, semantic queries, additional ML analytics \\ \hline
[28] & AdaBoost algorithm \\ \hline
[29] & Clustering, decision trees, association rule mining \\ \hline
[30] & Difference Factor K-NN collaborative filtering (DF-KNN) \\ \hline
[31] & Agglomerative clustering algorithms \\ \hline
[32] & Support Vector Machines (SVM), decision trees, inductive logic programming \\ \hline
[33] & SVM, decision trees, inductive logic programming \\ \hline
[34] & Non-linear kernel techniques \\ \hline
[35] & Gibbs sampling based latent Dirichlet allocation classifier framework \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Studies Employing ML Techniques for Improving Recommender Systems in E-commerce. The use of these algorithms allowed for the assessment of individual behaviors and classification of product reviews.