基于物品的改进协同过滤算法及应用
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摘要
针对电视产品信息资源量过载导致用户选择困难的问题,本文主要研究了基于物品的协同过滤算法在电视产品推荐系统中的改进及应用,将个性化推荐技术和电视产品系统有机结合来满足用户和运营商的需求.在推荐过程中,首先收集用户的偏好建立数据模型,以用户观看电视产品的时长作为用户偏好的显式特征,然后在传统的协同过滤算法中引入点播金额权重进行改进,并采用欧几里德距离法计算物品相似度,最后根据邻居集合预测目标用户对电视产品的观看时长,得到推荐结果.实验表明,通过引入点播金额权重这一改进能够提高推荐的准确性.
Aiming at the problem that the overload of information resources of TV products leads to the difficulty of user selection,this study mainly focuses on the improvement and application of article-based collaborative filtering algorithm in television product recommendation system,and combines the personalized recommendation technology with TV product system to meet the need of users and operations.In the recommendation process,the user's preference data model is first collected,and the duration of the user watching the television product is taken as the explicit characteristics of the user's preference.Then,it is improved by introducing the weight of on-demand amount in the traditional collaborative filtering algorithm,and the Euclidean distance method is used to calculate the similarity of the items.Finally,the viewing time of the target user on the television products is predicted according to the neighbor set,and a recommendation result is obtained.Experiments show that the introduction of on-demand amount weights can improve the accuracy of recommendations.
Aiming at the problem that the overload of information resources of TV products leads to the difficulty of user selection,this study mainly focuses on the improvement and application of article-based collaborative filtering algorithm in television product recommendation system,and combines the personalized recommendation technology with TV product system to meet the need of users and operations.In the recommendation process,the user's preference data model is first collected,and the duration of the user watching the television product is taken as the explicit characteristics of the user's preference.Then,it is improved by introducing the weight of on-demand amount in the traditional collaborative filtering algorithm,and the Euclidean distance method is used to calculate the similarity of the items.Finally,the viewing time of the target user on the television products is predicted according to the neighbor set,and a recommendation result is obtained.Experiments show that the introduction of on-demand amount weights can improve the accuracy of recommendations.
引文
1 Ying ZY, Zhou ZR, Han FJ, et al. Research on personalized Web page recommendation algorithm based on user context and collaborative filtering. Proceedings of the IEEE 4th International Conference on Software Engineering and Service Science. Beijing, China. 2013. 220-224.
2张光前,雷彩华,吕晓敏.电子商务推荐的研究现状及其发展前景.情报杂志,2011, 30(12):60-65.[doi:10.3969/j.issn.1671-3982.2011.12.021]
3黄贤英,龙姝言,谢晋.结合用户兴趣度聚类的协同过滤推荐算法.计算机应用研究,2019, 36(9).
4刘晓.基于隐式反馈的电视剧推荐系统[硕士学位论文].杭州:浙江大学,2015.
5孙红,韩震.融合物品热门因子的协同过滤改进算法.小型微型计算机系统,2018, 39(4):638-643.[doi:10.3969/j.issn. 1000-1220.2018.04.004]
6 Zheng S, Shen YJ, Zhang GD, et al. A collaborative filtering recommendation algorithm based on dynamic and reliable neighbors. Proceedings of the IEEE 6th International Conference on Software Engineering and Service Science.Beijing, China. 2015. 690-693.
7曹景振,贾新磊,李松丹.基于物品的协同过滤算法在ACM在线评测推荐系统中的改进及应用.无线互联科技,2018, 15(5):135-136, 139.[doi:10.3969/j.issn. 1672-6944.2018.05.062]
8张国凯.基于协同过滤的个性化服务推荐算法研究.软件导刊,2015, 14(10):43-44.
9刘文佳,张骏.改进的协同过滤算法在电影推荐系统中的应用.现代商贸工业,2018, 39(17):59-62.
10孙权,贺细平.协同过滤算法在ACM在线评测推荐系统中的应用研究.电脑与信息技术,2015, 23(6):11-14.[doi:10.3969/j.issn. 1005-1228.2015.06.004]
2张光前,雷彩华,吕晓敏.电子商务推荐的研究现状及其发展前景.情报杂志,2011, 30(12):60-65.[doi:10.3969/j.issn.1671-3982.2011.12.021]
3黄贤英,龙姝言,谢晋.结合用户兴趣度聚类的协同过滤推荐算法.计算机应用研究,2019, 36(9).
4刘晓.基于隐式反馈的电视剧推荐系统[硕士学位论文].杭州:浙江大学,2015.
5孙红,韩震.融合物品热门因子的协同过滤改进算法.小型微型计算机系统,2018, 39(4):638-643.[doi:10.3969/j.issn. 1000-1220.2018.04.004]
6 Zheng S, Shen YJ, Zhang GD, et al. A collaborative filtering recommendation algorithm based on dynamic and reliable neighbors. Proceedings of the IEEE 6th International Conference on Software Engineering and Service Science.Beijing, China. 2015. 690-693.
7曹景振,贾新磊,李松丹.基于物品的协同过滤算法在ACM在线评测推荐系统中的改进及应用.无线互联科技,2018, 15(5):135-136, 139.[doi:10.3969/j.issn. 1672-6944.2018.05.062]
8张国凯.基于协同过滤的个性化服务推荐算法研究.软件导刊,2015, 14(10):43-44.
9刘文佳,张骏.改进的协同过滤算法在电影推荐系统中的应用.现代商贸工业,2018, 39(17):59-62.
10孙权,贺细平.协同过滤算法在ACM在线评测推荐系统中的应用研究.电脑与信息技术,2015, 23(6):11-14.[doi:10.3969/j.issn. 1005-1228.2015.06.004]