Cross-Border E-Commerce Personalized Recommendation Based on Fuzzy Association Specifications Combined with Complex Preference Model

Abstract

Since cross-border e-commerce involves the export and import of commodities, it is affected by many policies and regulations, resulting in some special requirements for the recommendation system, which makes the traditional collaborative filtering recommendation algorithm less effective for the cross-border e-commerce recommendation system. To address this issue, a simple yet effective cross-border e-commerce personalized recommendation is proposed in this paper, which integrates fuzzy association rule and complex preference into a recommendation model. Under the constraint of fuzzy association rules, a hybrid recommendation model based on user complex preference features is constructed to mine user preference features, and personalized commodities recommendation is realized according to user behavior preference. Compared with the traditional recommendation algorithm, the improved algorithm reduces the impact of data sparsity. The experiment also verifies that the improved fuzzy association rule algorithm has a better recommendation effect than the existing state-of-the-art recommendation models. The recommendation system proposed in this paper has better generalization and has the performance to be applied to real-life scenarios.

An Optimization Model for Logistics Distribution Network of Cross-Border E-Commerce Based on Personalized Recommendation Algorithm

“…e performance of the proposed algorithm must be proved by experiments. So, two recently proposed methods, a simple yet effective cross-border e-commerce personalized recommendation that integrates fuzzy association rule and complex preference into a recommendation model (FACP) [26] which is also the Original model 1 and an optimization model of logistics distribution path based on IoT (LDIoT) [27] which is also the Original model 2, were employed. According to the FACP scheme, a hybrid recommendation model based on user complex preference features is constructed to mine user preference features, and personalized commodities recommendation is realized according to user behavior preference.…”

Fuzzy association rule mining approach to identify e-commerce product association considering sales amount

“…It is powerful to detect relationships between products, which meet specific support and confidence degrees. The discovered association rules are invaluable for B2C companies to offer products that a customer is interested in [6]. Association rules are created by counting products that are purchased together and trust of product relationships, respectively, called support and confidence.…”

“…A fuzzy extension of the traditional ARM method can close this gap [10]. Recent studies showed how fuzzy set theory could improve in evaluating the online customers' demand from a B2C company [6,[11][12][13].…”

“…FARM has been a popular method in recommendation systems in recent years [6]. It has been extended with additional properties to benefit from advantages of fuzziness to new problems such as profile-based fuzzy association rule mining (PB-FARM) approach [35] and class-based fuzzy soft associative (CBFSA) [36].…”

“…Especially, in e-commerce, the FARM method has been implemented because of much data without a clear boundary. At present, the advancement of the FARM has become a popular research topic [6]. The use of association rules can yield more meaningful information that can be used in the decision-making process.…”