With the advent of the era of big data, data mining has become one of the key technologies in the field of research and business. In order to improve the efficiency of data mining, this paper studies data mining based on the intelligent recommendation system. Firstly, this paper makes mathematical modeling of the intelligent recommendation system based on association rules. After analyzing the requirements of the intelligent recommendation system, Java 2 Platform, Enterprise Edition, technology is used to divide the system architecture into the presentation layer, business logic layer, and data layer. Recommendation module is divided into three substages: data representation, model learning, and recommendation engine. Then, the fuzzy clustering algorithm is used to optimize the system. After the system is built, the performance of the system is evaluated, and the evaluation indexes include accuracy, coverage, and response time. Finally, the system is put into a trial operation of an e-commerce platform. The click-through rate and purchase conversion rate of recommended products before and after the operation are compared, and a questionnaire survey is randomly launched to the platform users to analyze the user satisfaction. The experimental data show that the MAE of this system is the lowest, maintained at about 0.73, and its accuracy is the highest; before the recommended threshold exceeds 0.5, the average coverage rate of this system is the highest: 0.75; in Q1–Q5 subsets, the shortest response time of the system is 0.2 s. Before and after the operation of the system, the average click-through rate increased by 11.04%, and the average purchase rate increased by 9.35%. Among the 1216 users, 43% of the users were satisfied with 4 and 9% with 1. This shows that the system algorithm convergence speed is fast; it can recommend products more in line with user needs and interests and promote higher click-through rate and purchase rate, but user satisfaction can be further improved.

中文翻译：

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基于数学建模的个性化数据挖掘智能推荐系统

随着大数据时代的到来，数据挖掘已成为研究和商业领域的关键技术之一。为了提高数据挖掘的效率，本文研究了基于智能推荐系统的数据挖掘。首先，本文基于关联规则对智能推荐系统进行数学建模。在分析了智能推荐系统Java 2 Platform Enterprise Edition的需求之后，使用该技术将系统体系结构划分为表示层，业务逻辑层和数据层。推荐模块分为三个子阶段：数据表示，模型学习和推荐引擎。然后，使用模糊聚类算法对系统进行优化。构建系统后，对系统的性能进行评估，评估指标包括准确性，覆盖范围和响应时间。最后，该系统进入了电子商务平台的试运行。比较操作前后推荐产品的点击率和购买转化率，并随机向平台用户发起问卷调查，以分析用户满意度。实验数据表明，该系统的MAE最低，保持在0.73左右，精度最高。在推荐阈值超过0.5之前，该系统的平均覆盖率最高：0.75；在 比较操作前后推荐产品的点击率和购买转化率，并随机向平台用户发起问卷调查，以分析用户满意度。实验数据表明，该系统的MAE最低，保持在0.73左右，精度最高。在推荐阈值超过0.5之前，该系统的平均覆盖率最高：0.75；在 比较操作前后推荐产品的点击率和购买转化率，并随机向平台用户发起问卷调查，以分析用户满意度。实验数据表明，该系统的MAE最低，保持在0.73左右，精度最高。在推荐阈值超过0.5之前，该系统的平均覆盖率最高：0.75；在 该系统的平均覆盖率最高：0.75；在 该系统的平均覆盖率最高：0.75；在Q 1 – Q 5个子集，系统的最短响应时间为0.2 s。系统运行前后，平均点击率提高了11.04％，平均购买率提高了9.35％。在1216个用户中，有43％的用户对4表示满意，而9％的用户对1表示满意。它可以根据用户需求和兴趣推荐更多产品，并提高点击率和购买率，但是可以进一步提高用户满意度。