Predicting customer purchase behavior in the e-commerce context

International Journal of Innovative Science and Research Technology (IJISRT), 2019

As e-commerce platforms continue to expand, understanding consumer behavior has become crucial for enhancing customer satisfaction and driving business success. Recommendation systems play a pivotal role in predicting consumer preferences and delivering personalized product suggestions. This paper presents an extensive literature review on recommendation techniques, including collaborative filtering, content-based approaches, and hybrid models. Notable advancements, such as the use of deep learning, trust-based filtering, and context-aware models, are highlighted. Building on these foundations, we propose a novel model that integrates advanced machine learning algorithms with consumer behavior analysis to predict preferences more accurately. The expected results suggest that this model will improve the precision of recommendations, effectively addressing challenges like data sparsity and evolving user preferences and enhancing overall customer engagement in ecommerce environments.

International Journal Of Engineering And Computer Science, 2017

Customer who viewed this item wills also view that" is an intelligent as well as expert activity that keeps track of customers. Online shopping trends are based on the statistics which execute cross-selling mechanism. This intelligent algorithm will suggest your site visitors with products which were mostly explored by other customers on basis of maximum ratings, purchased or liked and viewed. These suggestions display on product pages that are based on the current product. Various Shopping application and web-sites are using different methods to attract customers. In this paper we are going to study the algorithm and introduction to machine learning approach to find how E-commerce uses recommendation techniques.

IAES International Journal of Artificial Intelligence, 2023

Nowadays, e-commerce is becoming an essential part of business for many reasons, including the simplicity, availability, richness and diversity of products and services, flexibility of payment methods and the convenience of shopping remotely without losing time. These benefits have greatly optimized the lives of users, especially with the technological development of mobile devices and the availability of the Internet anytime and anywhere. Because of their direct impact on the revenue of e-commerce companies, recommender systems are considered a must in this field. Recommender systems detect items that match the customer's needs based on the customer's previous actions and make them appear in an interesting way. Such a customized experience helps to increase customer engagement and purchase rates as the suggested items are tailored to the customer's interests. Therefore, perfecting recommendation systems that allow for more personalized and accurate item recommendations is a major challenge in the e-marketing world. In our study, we succeeded in developing an algorithm to suggest personal recommendations to customers using association rules via the Frequent-Pattern-Growth algorithm. Our technique generated good results with a high average probability of purchasing the next product suggested by the recommendation system.

International Journal of Engineering and Advanced Technology, 2021

In our day-to-day life, everyone settles on choices on whether to purchase an item or not. In a couple of cases, the choice depends on cost however on numerous occasions the buying choice is more intricate, still, numerous other reasons may be cogitated prior to the last decision is take. Within large-scale industries, understanding existing consumer’s purchasing behavior towards the product is more important to drive a business to the next level. In the context to expand the business on a large scale understanding, the customer interest is more important. To understand the behavior of customers and their interest in the products we need some new technologies and a large amount of data. In this paper we present a progression of examinations, investigate and analyze the exhibitions of various ML strategies, and talk about the meaning of the discoveries with regards to public arrangement and purchaser buying choice. Utilizing an enormous certifiable informational collection (which wil...

Computers, Materials & Continua

The COVID-19 has brought us unprecedented difficulties and thousands of companies have closed down. The general public has responded to call of the government to stay at home. Offline retail stores have been severely affected. Therefore, in order to transform a traditional offline sales model to the B2C model and to improve the shopping experience, this study aims to utilize historical sales data for exploring, building sales prediction and recommendation models. A novel data science life-cycle and process model with Recency, Frequency, and Monetary (RFM) analysis method with the combination of various analytics algorithms are utilized in this study for sales prediction and product recommendation through user behavior analytics. RFM analysis method is utilized for segmenting customer levels in the company to identify the importance of each level. For the purchase prediction model, XGBoost and Random Forest machine learning algorithms are used to build prediction models and 5-fold Cross-Validation method is utilized to evaluate their. For the product recommendation model, the association rules theory and Apriori algorithm are used to complete basket analysis and recommend products according to the outcomes. Moreover, some suggestions are proposed for the marketing department according to the outcomes. Overall, the XGBoost model achieved better performance and better accuracy with F1-score around 0.789. The proposed recommendation model provides good recommendation results and sales combinations for improving sales and market responsiveness. Furthermore, it recommend specific products to new customers. This study offered a very practical and useful business transformation case that assists companies in similar situations to transform their business models.

International Journal of Computer Science and Information Security (IJCSIS), Vol. 22, No. 3, June 2024, 2024

Algorithms are used in e-commerce product recommendation systems. These systems just recently began utilizing machine learning algorithms due to the development and growth of the artificial intelligence research community. This project aspires to transform how ecommerce platforms communicate with their users. We have created a model that can customize product recommendations and offers for each unique customer using cutting-edge machine learning techniques, we used PCA to reduce features and four machine learning algorithms like Gaussian Naive Bayes (GNB), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), the Random Forest algorithms achieve the highest accuracy of 99.6% with a 96.99 r square score, 1.92% MSE score, and 0.087 MAE score. The outcome is advantageous for both the client and the business. In this research, we will examine the model's development and training in detail and show how well it performs using actual data. Learning from machines can change of ecommerce world. Keywords: Machine Learning, Random Forest, Recommendations System, Decision Tree, PCA, E-commerce.

Collaborative filtering has been extensively studied in the context of ratings prediction. However, industrial recom-mender systems often aim at predicting a few items of immediate interest to the user, typically products that (s)he is likely to buy in the near future. In a collaborative filtering setting, the prediction may be based on the user's purchase history rather than rating information, which may be unreliable or unavailable. In this paper, we present an experimental evaluation of various collaborative filtering algorithms on a real-world dataset of purchase history from customers in a store of a French home improvement and building supplies chain. These experiments are part of the development of a prototype recommender system for salespeople in the store. We show how different settings for training and applying the models, as well as the introduction of domain knowledge may dramatically influence both the absolute and the relative performances of the different algorithms. To the best of our knowledge, the influence of these parameters on the quality of the predictions of recommender systems has rarely been reported in the literature.

International Journal of Advanced Networking and Applications

The trend of online-shopping has gradually increased and this trend is growing with a fast pace in the present scenario. As the trend of online-shopping is growing day by day, the prediction of consumer purchasing behavior and choices is becoming as a topic of curiosity for the researchers and business-organizations. It is very challenging to predict buying behaviour of clients in advance. The discovery of consumer purchase patterns in advance can be proven useful for increasing the growth of businesses and generation of revenue. This proposed research work is an effort to develop a framework that presents some useful insights and predicts consumers’ shopping behaviour by applying effective machine learning techniques.The present research work studies and analyses the various aspects and dimensions of online shopping which may impact the experience of purchasing by examining the considered data-set. Further, the thorough study of different machine-learning classification algorithms ...

2016

Collaborative filtering algorithms (CFAs) are recommender systems for the collaborating one another to filter documents they read from last decade. CFAs have various features that create them different from other algorithms. Algorithm of user-based collaborative filtering is one of filtering algorithms, known for their effectiveness and simplicity. In the present paper, pearson calculation is applied which works on user-based data and finds out the similarity measure of the products and then recommend them according to the similarity calculated. An application is built to perform this research work whose web pages have been attached as a part of results calculated. The model is an improved model working well on collaborative technique and recommending items to the users. Along with this few add-on implementations have been done so as to improve the functionality of the application.

International Journal on Customer Relations, 2021

Product recommender systems have been an effective approach to overcoming information overload on the Web, with the growing size of online statistics, as recommending the right product based on consumer liking became challenging for e-commerce businesses. The machine learning techniques can be applied to solve it. However, due to the large number of algorithms available in the literature, it is quite difficult to select a suitable machine learning algorithm. Researchers have little information about the best approaches to develop recommender systems for e-commerce using machine learning. Here, we have presented our work as a systematic review of the literature, which surveys to choose machine learning algorithms to recommend products in e-commerce and recognise research opportunities for the researchers in developing recommender systems. The survey concluded that deep learning and neural networks techniques are widely used to predict the right products for recommendation to the customers in e-commerce, because they can be very good at recognising patterns in a way similar to the human brain.