基于社会化标签和历史价格曲线的网络结构个性化推荐方法  

Network Structure Personalized Recommendation Method Based on Social Tag and Historical Price Curve

作　　者：凌霄娥[1] 

机构地区：[1]广西民族师范学院图书馆,广西崇左532200

出　　处：《计算机工程》2017年第4期212-216,共5页Computer Engineering

基　　金：广西壮族自治区教育厅科研项目(201204LX481);广西壮族自治区文献信息服务中心预研项目(CALISGX201305)

摘　　要：社会化标签包含商品丰富的内容,体现用户对商品的个性偏好,而商品的历史价格曲线影响用户的购买倾向。为此,提出社会化标签和历史价格曲线的个性化推荐方法。将用户-标签-商品的三部图网络结构分解为用户-标签、标签-商品的二部图网络结构。在用户-标签二部图中,利用商品历史价格曲线的权重,基于商品历史价格曲线求得用户对标签的评分,并区分用户对标签的积极和消极兴趣。在标签-商品的二部图中,计算标签对商品的重要程度。综合2种方法进行商品推荐,实验结果验证了提出方法能够有效提高推荐的召回率和多样性。Social tag contains rich content of commodity and reflects user＇ s personal preference for commodity. Historical price curve of commodity impacts user＇ s purchase-tendency. Therefore, this paper proposes a personalized recommendation method based on social tag and historical price curve. It breaks up user-tag-commodity tripartite graph network into user-tag and tag-commodity bipartite graph network. In user-tag bipartite graph, weight of historical commodity price curve is used to compute score based on the historical price curve, and positive interest is distinguished from negative one. In tag-commodity bipartite graph, weight of tag to commodity is computed. The two methods are synthesized for commodity recommendation. Experimental result verifies the proposed method can improve the recall and diversity of recommendation effectively.

关 键 词：社会化标签 历史价格曲线 网络结构 个性化推荐 二部图 三部图 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]