| 1. | Research of personalized recommender system based on data mining on magnanimity data
基于海量数据挖掘的个性化推荐系统 |
| 2. | A survey of collaborative filtering algorithm applied in e - commerce recommender system
电子商务推荐系统中的协同过滤推荐 |
| 3. | Model of collaborative filtering in e - commerce recommender system based on interest measure
基于兴趣度的协同过滤商品推荐系统模型 |
| 4. | Collaborative filtering is a successful technology that is implemented in e - commerce recommender systems today
协同过滤是目前在电子商务推荐系统中应用较为成功的个性化推荐技术。 |
| 5. | Also in the study of recommender system , we noticed mass marketing has been replaced by data - driven one to one marketing
此外在对推荐系统的研究中我们注意到，进入网络经济时代，大众营销策略已经没落。 |
| 6. | It gives emphasis to analyzing the problems which collaborative filtering is facing when it is applied in recommender systems and existing improved methods
着重分析了协同过滤在推荐系统中应用时所面临的问题，以及现有的解决方法。 |
| 7. | Web personalized recommender systems anticipate the needs of web users and provide them with recommendations according to their navigation patterns
Web个性化推荐系统根据用户的浏览模式预测用户需求,并向他们提供个性化的推荐服务。 |
| 8. | Due to the enormous good of recommender systems , a lot of e - commerce businesses began to utilize this technology to assist their customers buying process
因此，推荐系统可谓面向电子商务企业与用户的双赢策略。推荐系统的研究是以顾客购买行为的分析为基础的。 |
| 9. | The paper introduces e - commerce recommender systems and the typical technologies that are implemented in them , collaborative filtering technology and its two directions of existing algorithms
论文全面介绍了电子商务推荐系统及其典型的实现技术、协同过滤及已有协同过滤算法的两个方向。 |
| 10. | In order to make the problems solved and improve the quality and efficient of recommender systems , some researches have been done , and the research results have been successfully applied in practice
为了迎接这种挑战，提高推荐系统的推荐质量和实时性，目前国内外进行了较多的研究，其成果也在实际中得到了一定的应用。 |
