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A hybrid collaborative filtering recommendation model-based on complex attribute of goods

机译：基于商品复杂属性的混合协同过滤推荐模型

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摘要

Collaborative filtering as the most widely used, the most recommendation algorithm, the shortcomings inherent in the data sparse, cold start and others has been greatly improved, but few studies based on commodity price to improve the prediction accuracy. At the same time, facing the full e-commerce market network Navy, the ratings and reviews also indirectly led to reducing the accuracy of prediction. Therefore, considering comprehensively the subjective scoring of users and the objective scoring of products, the paper puts forward a hybrid collaborative filtering recommendation model by combing situational pre-filtering, social network theories and experts' opinions. And through experiments, the model has higher forecast accuracy than the traditional collaborative filtering, and is more suitable for the commodity with complex attributes.
机译：协同过滤作为应用最广泛，推荐性最强的算法，在数据稀疏，冷启动等方面固有的缺点得到了很大的改善，但是很少有基于商品价格的研究来提高预测精度。同时，面对完整的电子商务市场网络海军，评级和评论也间接导致降低了预测的准确性。因此，综合考虑用户的主观评分和产品的客观评分，结合情境预过滤，社交网络理论和专家意见，提出了一种混合协同过滤推荐模型。并且通过实验，该模型比传统的协同过滤具有更高的预测准确度，更适用于属性复杂的商品。

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