文章基本信息

标题：A New Approach to Travel Recommendation using Dynamic Topic Model and Matrix Factorization
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作者：Harshvardhan Hanmnatrao Patil ; Prof. S.D. Satav
期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2018
卷号：7
期号：7
页码：7694-7701
DOI：10.15680/IJIRSET.2018.0707015
出版社：S&S Publications
摘要：On web the rapid growth of online travel information have to face for tourists who have to choose from a large number of travel available packages to satisfy their personalized requirements to travel. The sparsity of user and location interactions makes it difficult to learn travel preferences, because a user usually visits only a limited number of travel locations. Static topic models can be used to solve the sparsity problem by considering user travel topics. However, all travel histories of a user are regarded as one document drawn from a set of static topics, ignoring the evolving of topics and travel preferences. In this paper, we propose a dynamic topic model (DTM) and matrix factorization (MF) based travel recommendation method. A DTM is used to obtain the temporally fine-grained topic distributions (i.e., implicit topic information) of users and locations. In addition, a large amount of explicit information is extracted from the metadata and visual contents of CCGPs, Check-ins, and POI categories datasets. The information is used to obtain user-user and location-location similarity information, which is a imposed as two regularization terms to constraint MF. User can view or get recommended places route on map. User’s preference in mined through Lavenstine Distance Algorithm.
关键词：geo;tagged photos; check;in record; dynamic topic model; multimedia information retrieval; social