Efficient Hybrid Web Recommendations Based on Markov Clickstream Models and Implicit Search
Efficient Hybrid Web Recommendations Based on Markov Clickstream Models and Implicit Search
复制标题
基于马尔可夫点击流模型和隐式搜索的高效混合网络推荐
DOI：
10.1109/wi.2007.128
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
Xiangji Huang
中科院分区：
文献类型：
--
作者：
Yiyu Yao;Yi Zeng;N. Zhong;Xiangji Huang
在本文中，我们提出了结合 (1) 马尔可夫模型和 (2) 网页内容搜索技术来生成 Web 导航推荐的新颖方法。对于点击流建模，研究了一阶和二阶马尔可夫模型，并使用马尔可夫转移矩阵的紧凑存储格式。对于基于内容的搜索，利用搜索引擎获取相似内容的页面进行推荐，以弥补马尔可夫模型的稀疏性，从而提高覆盖率。在真实的网络点击流日志上进行了实验，并证实了所提出方法的有效性。
In this paper, we present novel methods that combine (1) Markov models and (2) Web page content search techniques to generate Web navigation recommendations. For click-stream modeling, both first-order and second-order Markov models were studied and a compact storage format for Markov transition matrices was used. For content-based search, a search engine was used to obtain similar-content pages for recommendation to compensate for the sparsity of the Markov model and thus improve coverage. Experiments were conducted on real Web clickstream logs, and confirmed the efficiency of the proposed methods.