Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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

Practical implications

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.

Originality/value

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.

中文翻译：

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一个有效的推荐系统，该系统基于时间特征下的客户的性格特征，人口统计和行为

目的

推荐系统（RSs）的性能提高一直是电子商务领域的主要挑战，因为这些系统面临着诸如冷启动，稀疏性，可伸缩性和兴趣漂移之类的问题，这些问题会影响其性能。尽管已为解决这些问题做出了努力，但仍然没有RS可以同时解决或减少所有问题。因此，本研究的目的是提供一种有效且全面的RS，以解决或减少上述所有问题，它结合了基本的客户信息和大数据技术。

设计/方法/方法

建议的RS中最重要的步骤是：（1）从电子服装商店收集顾客的人口统计和行为数据；（2）评估顾客的个性特征；（3）基于客户/用户兴趣创建新的用户项目矩阵；（4）使用基于位置敏感的哈希（LSH）方法的高效k最近邻（EKNN）算法计算客户之间的相似度，以及（5）基于人格特质，人口统计特征和时间组合确定新的相似度函数基于购买行为，这是促成客户购买的主要诱因。

发现

将该方法与不同的基线（矩阵分解和集成）进行了比较。结果表明，在所有评估措施方面，所提出的方法显着改善了传统协作过滤（CF）性能，并且有显着差异（超过40％），其性能优于所有基线。根据结果​​，我们发现我们提出的方法结合了个性信息和人口统计信息，并通过LSH方法跟踪了客户的近期兴趣和需求，它比建议的方法更能提高建议的有效性。基线。这是由于以下事实：这种方法结合了LSH技术，结合了上述信息，可以更有效，更准确地解决冷启动，可扩展性，

研究局限/意义

研究数据仅限于一家电子服装商店。

实际影响

为了在电子商务中获得准确和实时的RS，必须结合使用客户信息和有效的技术。在这方面，根据研究结果，使用人格特质和人口统计特征可以更准确地了解客户的兴趣，从而更好地识别相似的客户。因此，应将此信息视为减少冷启动和稀疏性问题的解决方案。另外，可以通过考虑客户最近的购买来更好地判断他们的兴趣；因此，为了解决利益漂移的问题，应该在不同时间（产品越多，权重越高）对产品/商品的购买和发布时间分配不同的权重。最后，LSH技术用于增加电子商务中的RS可伸缩性。总体而言，应使用LSH技术结合个性特征，人口统计和随着时间的推移的客户购买行为，以实现理想的RS。使用这项研究中提出的RS，可以通过提供与客户喜好相匹配的实时建议来为客户创造舒适愉悦的购物体验，并可以提高电子商店的获利能力。

创意/价值

在这项研究中，结合LSH技术，结合了人格特质，人口统计学特征和基于时间的客户购买行为，我们首次能够同时解决CF的基本问题，即冷启动，可扩展性，稀疏性和兴趣漂移，导致推荐的重大错误减少，CF的准确性增加。根据所提出的模型提供给用户的建议的平均错误率仅为13％，而这些建议的准确性和符合客户利益的程度约为92％。此外，已发现该方法与传统CF方法的精度之间存在40％的差异。RS中的这种准确性水平非常重要和特殊，这当然受到电子商务所有者的欢迎。这也是一个新的科学发现，对程序员，用户和研究人员非常有用。总的来说，这项研究的主要贡献是：1）利用人格特质，人口统计学特征和基于时间的购买行为提出准确的RS；2）为“服装”在线商店提出有效而全面的RS；3）通过使用性格特征和人口统计学特征解决冷启动问题，从而提高RS的性能；4）通过有效地改善RS中的可伸缩性问题 人口特征和基于时间的购买行为；2）为“服装”在线商店提出有效而全面的RS；3）通过使用性格特征和人口统计学特征解决冷启动问题，从而提高RS的性能；4）通过有效地改善RS中的可伸缩性问题 人口特征和基于时间的购买行为；2）为“服装”在线商店提出有效而全面的RS；3）通过使用性格特征和人口统计学特征解决冷启动问题，从而提高RS的性能；4）通过有效地改善RS中的可伸缩性问题k-最近邻居；5）通过使用人格特质和人口统计学特征以及还通过对用户项目矩阵进行增密来缓解稀疏性问题； 6）通过开发基于时间的用户项目矩阵来解决兴趣漂移问题，从而提高RS准确性。