An effective recommender system based on personality traits, demographics and behavior of customers in time context

Abstract

PurposeImproving 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.Design/methodology/approachThe 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.FindingsThe 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.Research limitations/implicationsThe research data were limited to only one e-clothing store.Practical implicationsIn 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.Originality/valueIn 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.

Contextual Personality-Aware Recommender System Versus Big Data Recommender System

“…Finally, RS for e-clothing store based on personality traits, demographics, and behavior of customers in time context was presented by [37]. Their proposed method was compared with different baselines (matrix factorization and ensemble).…”

News recommendations based on collaborative topic modeling and collaborative filtering with generative adversarial networks

“…To solve this problem, recommender systems (Jakheliya et al , 2019; Ruby Annette et al , 2019) have been researched for years to effectively find hidden rules behind users' logs, including their personal profile, browsing history, ratings, comments, etc. Recommender systems can mainly be divided into collaborative filtering (CF) (Khodabandehlou et al , 2020; Su and Khoshgoftaar, 2009; Breese et al , 1998) and content-based filtering (CBF) (Basu et al , 1998; Melville et al , 2002; Lops et al , 2011). CF algorithms such as matrix factorization (MF) (Koren et al , 2009) decompose the user–item rating matrix into the product of two lower dimensions of user and item latent features and make prediction through the latent features of users and items.…”

Impact on recommendation performance of online review helpfulness and consistency

“…However, there is an information overload problem in that the cost of information search increases for customers making purchase decisions. In other words, Impact of online review on recommender system selecting an item suited to customer preference from among many items takes a long time and is challenging (Park et al, 2012;Su and Khoshgoftaar, 2009;Khodabandehlou et al, 2020).…”

“…The collaborative filtering (CF) algorithm is the most widely used of many recommender systems (Kim et al, 2012(Kim et al, , 2010bKhodabandehlou et al, 2020). CF algorithms are implemented based on the following assumption: customers with similar preferences for certain items exhibit similar preferences for other items.…”

“…However, there is an information overload problem in that the cost of information search increases for customers making purchase decisions. In other words, selecting an item suited to customer preference from among many items takes a long time and is challenging (Park et al , 2012; Su and Khoshgoftaar, 2009; Khodabandehlou et al , 2020). Recently, the demand for online shopping has soared; however, customers face limitations in checking and experiencing their preferred items or services, which highlights the problem of information overload.…”