[1]梁耀培,吴定明.基于位置的个性化关键词查询推荐[J].深圳大学学报理工版,2019,36(No.4(347-472)):467-472.[doi:10.3724/SP.J.1249.2019.04467]
 LIANG Yaopei and WU Dingming.Location-aware personalized keyword query recommendation[J].Journal of Shenzhen University Science and Engineering,2019,36(No.4(347-472)):467-472.[doi:10.3724/SP.J.1249.2019.04467]
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基于位置的个性化关键词查询推荐()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第36卷
期数:
2019年No.4(347-472)
页码:
467-472
栏目:
【电子与信息科学】
出版日期:
2019-07-10

文章信息/Info

Title:
Location-aware personalized keyword query recommendation
作者:
梁耀培吴定明
深圳大学计算机与软件学院,广东深圳 518060
Author(s):
LIANG Yaopei and WU Dingming
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, P.R.China
关键词:
人工智能数据库数据结构关键词推荐个性化随机漫步空间坐标数据二部图
Keywords:
artificialintelligence database data structurekeywordrecommendation personalization random walk spatial data bi-partite graph
分类号:
TP319
DOI:
10.3724/SP.J.1249.2019.04467
文献标志码:
A
摘要:
查询推荐是指根据用户的输入提供若干替代的查询,用户使用推荐的查询去检索,得到更多符合需求的信息.利用基于位置的关键词查询推荐所提供的替代关键词能够检索到在用户查询位置附近的信息.用户提交的关键词常是多义词且含有各自的背景偏好,采用具有个性化的推荐查询则能检索到符合用户偏好的信息.为同时满足空间位置邻近和个性化需求,提出一种基于位置的个性化关键词查询推荐方法,使推荐查询的关键词能够检索到位于用户附近且符合其偏好的信息.用关键词-文档二部图表示不同关键词查询之间的语义相似性,采用动态边权重调整策略,建立与关键词相关的文档和用户当前位置的空间关系,使用分类向量模型表示用户的兴趣爱好,应用带重启的随机漫步模型,得到与用户输入的关键词具有较高相似度的其他关键词.在AOL真实数据集上的测试结果表明,该方法为用户推荐的关键词不仅可以满足用户的信息需求,还可以检索到用户位置附近符合其偏好的文档.
Abstract:
The query recommendation provides several alternative queries based on the input query. By using the recommended queries, the users may retrieve more relevant information. Location-aware keyword query recommendation aims for suggesting queries which are able to retrieve the relevant information close to the user’s location. When the submitted queries are ambiguousand have various background preferences, the personalized recommendation queries can retrieve information that meets users’ preferences. This paper studies a new method of query recommendation, i.e., the location-aware personalized keyword query recommendation. The queries suggested by this approach are able to retrieve nearby relevant information that matches the users’ preferences. The proposed method establishes the semantic relationships among keyword queries via a keyword-document bipartite graph. The weights of edges in the keyword-document bipartite graph are dynamically adjusted to represent the spatial proximity of documents. The users’ preferences are modeled by the category-based vectors. The random walk with restart model is used to compute recommended queries. This paper develops an efficient algorithm and data structures for the computation ofrecommendations. The experiments on AOL a real data set demonstrate the effectiveness of the proposed method.

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更新日期/Last Update: 2019-07-04