Tourism destination recommender system for the cold start problem
Tourism destination recommender system for the cold start problem
复制标题
旅游目的地推荐系统的冷启动问题
DOI：
10.3837/tiis.2016.07.018
复制
发表时间：
2016
影响因子：
1.5
通讯作者：
Lin Lu
中科院分区：
文献类型：
--
作者：
Xiaoyao Zheng;Yonglong Luo;Zhiyun Xu;Qingying Yu;Lin Lu
随着电子商务的出现和普及，越来越多的消费者喜欢在网上订购旅游产品。推荐系统可以帮助这些用户应对信息过载，然而，这样的系统受到冷启动问题的影响。在线旅游目的地搜索由于其限制因素较多，是一项比较困难的任务。因此，在本文中，我们提出了一个旅游目的地推荐系统，采用意见挖掘技术，以改善用户的喜好和项目的意见声誉。这些元素，然后融合成一个混合的协同过滤方法相结合的用户和项目为基础的协同过滤方法。同时，我们在推荐系统中嵌入了人工交互模块，以缓解冷启动问题。与几种著名的冷启动推荐方法相比，我们的方法提供了改进的推荐精度和质量。一系列的实验评估使用公开的数据集表明，建议的推荐系统优于现有的推荐系统在解决冷启动问题。
With the advent and popularity of e-commerce, an increasing number of consumers prefer to order tourism products online. A recommender system can help these users contend with information overload; however, such a system is affected by the cold start problem. Online tourism destination searching is a more difficult task than others on account of its more restrictive factors. In this paper, we therefore propose a tourism destination recommender system that employs opinion-mining technology to refine user preferences and item opinion reputations. These elements are then fused into a hybrid collaborative filtering method by combining user- and item-based collaborative filtering approaches. Meanwhile, we embed an artificial interactive module in our recommender system to alleviate the cold start problem. Compared with several well-known cold start recommendation approaches, our method provides improved recommendation accuracy and quality. A series of experimental evaluations using a publicly available dataset demonstrate that the proposed recommender system outperforms existing recommender systems in addressing the cold start problem.