CAJ | 학술논문

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。
위제고망락서점상사성수색효솔，강저시간화존저개소이괄응대규모수거，제출일충기우 P‐Rank적상사성수색우화산법ProductP‐Rank。대상사성수색산법진행분석화비교，지출상사성계산정학도화복잡도시현유산법소면림적난점；의거소비자여도서지간적구매관계구건구물망락，리선계산일보상사성구진，재선계산량보상사성구진。실험결과표명，해방법강저료상사성계산적존저화예계산시간적개소，구유교고정학도，능구쾌속향응사순청구。
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 simi‐larity 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 ma‐trix .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 .