摘要
兴趣点(Point-Of-Interest,POI)推荐是基于位置的社交网络(Location-based Social Networks,LBSNs)研究中最重要的任务之一.为了解决POI推荐中的空间稀疏性问题,提出一种用于位置推荐的长短期偏好时空注意力网络(LSAN).首先,构建了签到序列的时空关系矩阵,使用多头注意力机制从中提取非连续签到和非相邻位置中的时空相关性,缓解签到数据稀疏所带来的困难.其次,在模型中设置用户短期偏好和长期偏好提取模块,自适应的将二者结合在一起,考虑了用户偏好对用户决策影响.最后,在Foursquare数据集上进行测试,并与其它模型结果进行对比,证实了提出的LSAN模型结果最优.研究表明LSAN模型能够获得最佳的推荐效果,为POI推荐提供新思路.
Abstract
Point-Of-Interest(POI)recommendation is one of the most important tasks in Location-based Social Networks(LBSNs)research.In order to solve the spatial sparsity problem in POI recommendation,this study proposes a long and short-term preference attention network(LSAN)for location recommendation.Firstly,a relationship matrix of check-in sequences is constructed,which extracts the correlations in non-consecutive check-ins and non-adjacent positions using a multi-headed attention mechanism to alleviate the difficulties caused by sparse check-in data.Mo-reover,the short-term preference and long-term preference extraction modules are set in the model,which adaptively combines the two and considers the influence of user preferences on user decisions.Lastly,testing on the Foursquare dataset and comparing the results with other models confirmed the optimal results of the LSAN model proposed in this paper.The study indicates that the LSAN model can obtain the best recommendation effect and provide new ideas for POI recommendation.
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