基于P-Rank的网络书店相似性搜索

Online bookstore similarity search based on P-Rank

为提高网络书店相似性搜索效率,降低时间和存储开销以适应大规模数据,提出一种基于P-Rank的相似性搜索优化算法ProductP-Rank。对相似性搜索算法进行分析和比较,指出相似性计算精确度和复杂度是现有算法所面临的难点;依据消费者与图书之间的购买关系构建购物网络,离线计算一步相似性矩阵,在线计算两步相似性矩阵。实验结果表明,该方法降低了相似性计算的存储和预计算时间的开销,具有较高精确度,能够快速响应查询请求。

To increase the efficiency of algorithms on online bookstore＇s similarity search,and reduce time and space cost to adapt to large information network,ProductP-Rank,an optimized similarity search method,was proposed based on the basic idea of P-Rank.The past algorithms for similarity search were analyzed and discussed and the accuracy and complexity problems in similarity search were pointed out.By building the customer-product network according to the co-purchasing relationship,for a given query,the 2-hop similarity matrix between query and each item was computed based on the pre-computed 1-hop similarity matrix.Experimental results show the space cost and pre-computation time cost of ProductP-Rank were evidently less than that of P-Rank with little effectiveness loss and low online-query time cost.