| 摘 要: 伴随着网购的不断发展，各大电商网站均引入了商品推荐系统。但消费者却常常对这类系统的有效性抱 有疑问，因为他们发现自己前一天刚买的商品依然出现在今天的推荐列表中，而自己已经不再需要这类商品了。针对这 样的情况，需要给推荐系统增加一个过滤模块，将一些在当前明显不会被目标用户所需要的商品去除。本文在前人提出 的回购周期去重方案的基础上进行优化，提出了一套综合去重方案。 |
| 关键词: 推荐方法；推荐去重；推荐过滤；大数据 |
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中图分类号: TP391
文献标识码: A
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| Commodity Recommendation and Filtering Scheme Based on Consumer Shopping Records |
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ZHANG Pengcheng,MA Jialin
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( College of Software, Shenyang Normal University, Shenyang 110000, China)
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| Abstract: With the continuous development of online shopping,the main e-commerce websites have all introduced commodity recommendation systems.However,consumers usually have doubts on the effectiveness of such kind of system,as they find that the commodities they purchased a day before still appear on the recommendation list today,which they do not need any more.Aimed at such cases,a filtering module needs to be added to the recommendation system to eliminate the commodities that are obviously undesired by the target users at present.This paper conducted optimization based on the filtering schemes put forward on buy-back cycle previously and put forward a set of comprehensive filtering scheme. |
| Keywords: Recommendation method;repetition removal;recommendation filtering;big data |
