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.
Highlights
Association RuleAs an important research content in the cross-border e-commerce enterprise, the association rule has been promoted in various industries, and it has quickly become a very popular research field, where the more typical algorithms are Apriori and FP-growth [26]
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
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
Summary
As an important research content in the cross-border e-commerce enterprise, the association rule has been promoted in various industries, and it has quickly become a very popular research field, where the more typical algorithms are Apriori and FP-growth [26]. Fuzzy association rules need to solve the following problems: (1) Find the required frequent itemsets in crossborder e-commerce datasets by an iterative method. E mining of association rules is to find the lowest support and the lowest confidence interval in a cross-border e-commerce dataset, where the frequent itemset is found by calculating candidate sets, and strong association rules are obtained through frequent candidate sets. E recommendation system in cross-border e-commerce needs to obtain all the frequent sets from 1 to n, which is all the frequent itemsets we require. It can be seen that, in the recommendation process, the first problem is the key step to improve the recommendation accuracy and the performance of the recommendation is determined by the first step It may be affected by various aspects: firstly, too large candidate itemsets are generated. In order to calculate the minimum support of each frequent candidate set, we need to repeat the traversal data many times when looking for frequent itemsets from the candidate set
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