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Research on Goods Recommendation Strategy based on Decision Tree

机译：基于决策树的商品推荐战略研究

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Firstly, associative-sets-based collaborative filtering algorithm is proposed. During the process of personalized recommendation, some items evaluated by users are performed by accident, in other words, they have little correlation with users' real preferences. These irrelevant items are equal to noise data, and often interfere with the effectiveness of collaborative filtering. A personalized recommendation algorithm based on Associative Sets is proposed in this paper to solve this problem. It uses frequent it sets to get associative sets, and makes recommendations according to users' real preferences, so as to enhance the accuracy of recommending results. Test results show that the new algorithm is more accurate than the traditional. Secondly, a flexible E-Commerce recommendation system is built. Traditional recommendation system is a sole tool with only one recommendation model. In e-commerce environment, commodities are very rich, personal demands are diversification; E-Commerce systems in different occasions require different types of recommended strategies. For that, we analysis the recommendation system with flexible theory, and proposed a flexible e-commerce recommendation system. It maps the implementation and demand through strategy module, and the whole system would be design as standard parts to adapt to the change of the recommendation strategy.
机译：首先，提出了基于关联集的协同滤波算法。在个性化推荐过程中，用户评估的某些项目由事故执行，换句话说，它们与用户的真实偏好几乎没有相关性。这些无关的物品等于噪声数据，并且经常干扰协作滤波的有效性。在本文中提出了一种基于关联集的个性化推荐算法来解决这个问题。它使用频繁设置为获取关联集，并根据用户的真实偏好提出建议，以提高推荐结果的准确性。测试结果表明，新算法比传统更准确。其次，建立了一个灵活的电子商务推荐系统。传统推荐系统是一个只有一个推荐模型的唯一工具。在电子商务环境中，商品非常丰富，个人需求是多元化的;不同场合的电子商务系统需要不同类型的推荐策略。为此，我们分析了灵活理论的推荐系统，并提出了灵活的电子商务推荐系统。它通过策略模块映射实现和需求，整个系统将设计为标准零件，以适应推荐策略的变化。

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