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In this paper, a Quality of Service-sensitive customer behavior model graph (QoS-CBMG) is proposed for use in service quality adaptation in e-commerce systems. Success in achieving customer satisfaction and maximizing profit in e-commerce is highly dependent on the QoS provided. However, providing high-level QoS for all customers in all Web sessions is often deemed costly and inefficient. Therefore, a QoS-sensitive model for formulating QoS-aware offers to customers is required. The paper aims to respond to this necessity.

Process mining is adopted as the knowledge extraction technique for developing a QoS-CBMG. If it is assumed that user navigation on a website is a process, then clickstreams during one user’s navigations can be considered process steps.

The application of both QoS-CBMG (the new model) and CBMG (the classic version) to the same real data set demonstrated that the proposed method outperforms CBMG due to its reduction of average absolute error in the measurement scale. This finding also verifies the assumption that customer behavior is sensitive to the level of QoS.

From a theoretical viewpoint, the obtained QoS-CBMG facilitates the adaption in e-commerce systems, which leads to conduct the user to the desired behavior by tuning QoS levels in different Web sessions in a dynamic manner. This implication is due to the fact that QoS-CBMG can predict the upcoming clickstream of the customer at different QoS levels.

Using the proposed model for the adaptation of service quality in e-commerce websites not only results in the efficient management of the provider’s resources but also encourages customer purchases from the website and increases profitability. It is noteworthy that with the advent of cloud computing, e-commerce websites are enabled to provide various levels of QoS for their customers by supplying their basic services (e.g. infrastructure, platform) through cloud platforms.

According to the best of our knowledge, no previous model has taken into account the QoS dimension for customer behavior modeling. The main contribution of this paper is to propose a CBMG that is sensitive to the QoS provided to customers during their navigation to formulate QoS-aware offers to them.

Improving the performance of recommender systems (RSs) has always been a major challenge in the area of e-commerce because the systems face issues such as cold start, sparsity, scalability and interest drift that affect their performance. Despite the efforts made to solve these problems, there is still no RS that can solve or reduce all the problems simultaneously. Therefore, the purpose of this study is to provide an effective and comprehensive RS to solve or reduce all of the above issues, which uses a combination of basic customer information as well as big data techniques.

The most important steps in the proposed RS are: (1) collecting demographic and behavioral data of customers from an e-clothing store; (2) assessing customer personality traits; (3) creating a new user-item matrix based on customer/user interest; (4) calculating the similarity between customers with efficient k-nearest neighbor (EKNN) algorithm based on locality-sensitive hashing (LSH) approach and (5) defining a new similarity function based on a combination of personality traits, demographic characteristics and time-based purchasing behavior that are the key incentives for customers' purchases.

The proposed method was compared with different baselines (matrix factorization and ensemble). The results showed that the proposed method in terms of all evaluation measures led to a significant improvement in traditional collaborative filtering (CF) performance, and with a significant difference (more than 40%), performed better than all baselines. According to the results, we find that our proposed method, which uses a combination of personality information and demographics, as well as tracking the recent interests and needs of the customer with the LSH approach, helps to improve the effectiveness of the recommendations more than the baselines. This is due to the fact that this method, which uses the above information in conjunction with the LSH technique, is more effective and more accurate in solving problems of cold start, scalability, sparsity and interest drift.

The research data were limited to only one e-clothing store.

In order to achieve an accurate and real-time RS in e-commerce, it is essential to use a combination of customer information with efficient techniques. In this regard, according to the results of the research, the use of personality traits and demographic characteristics lead to a more accurate knowledge of customers' interests and thus better identification of similar customers. Therefore, this information should be considered as a solution to reduce the problems of cold start and sparsity. Also, a better judgment can be made about customers' interests by considering their recent purchases; therefore, in order to solve the problems of interest drifts, different weights should be assigned to purchases and launch time of products/items at different times (the more recent, the more weight). Finally, the LSH technique is used to increase the RS scalability in e-commerce. In total, a combination of personality traits, demographics and customer purchasing behavior over time with the LSH technique should be used to achieve an ideal RS. Using the RS proposed in this research, it is possible to create a comfortable and enjoyable shopping experience for customers by providing real-time recommendations that match customers' preferences and can result in an increase in the profitability of e-shops.

In this study, by considering a combination of personality traits, demographic characteristics and time-based purchasing behavior of customers along with the LSH technique, we were able for the first time to simultaneously solve the basic problems of CF, namely cold start, scalability, sparsity and interest drift, which led to a decrease in significant errors of recommendations and an increase in the accuracy of CF. The average errors of the recommendations provided to users based on the proposed model is only about 13%, and the accuracy and compliance of these recommendations with the interests of customers is about 92%. In addition, a 40% difference between the accuracy of the proposed method and the traditional CF method has been observed. This level of accuracy in RSs is very significant and special, which is certainly welcomed by e-business owners. This is also a new scientific finding that is very useful for programmers, users and researchers. In general, the main contributions of this research are: 1) proposing an accurate RS using personality traits, demographic characteristics and time-based purchasing behavior; 2) proposing an effective and comprehensive RS for a “clothing” online store; 3) improving the RS performance by solving the cold start issue using personality traits and demographic characteristics; 4) improving the scalability issue in RS through efficient k-nearest neighbors; 5) Mitigating the sparsity issue by using personality traits and demographic characteristics and also by densifying the user-item matrix and 6) improving the RS accuracy by solving the interest drift issue through developing a time-based user-item matrix.