Integrate Influence of Regions and Friends for Next POI Recommendation

doi:10.3778/j.issn.1673-9418.2009087

Abstract

Abstract:

With the increasing popularity of mobile devices, a large amount of user check-in point of interests (POIs) data have been accumulated. The information of user check-in makes the recommendation of the next POI become a hot issue in recent years. The accuracy of the next POI recommendation is restricted by two aspects: On the one hand, the sparsity of check-in data. At present, most researchers alleviate data sparsity problem to a certain extent by introducing geographic correlation or friend evaluation information on POIs. However, not all POIs have strong geographic correlation, and only a small number of users comment on POIs where they have check-ins. On the other hand, deep learning based check-in sequence training has the problem of gradient disappearing. In order to solve these problems, this paper proposes a user??s next POI recommendation model which integrates the influence of regions and friends. Firstly, the region information of POIs is integrated into the sequence of POIs. Sequentially, this paper uses the neural network model with residual connection to embed the sequence, to avoid gradient vanishing and improve the convergence of the model. Finally, this model integrates the information of POIs visited by friends for recommendation, to improve the accuracy of POI recommendation. Experimental results show that the proposed model is more accurate than other existing models.

Key words: point of interest (POI), POI recommendation, social relationship, region, residual network

摘要：

随着移动设备的日益普及，积累了大量的用户签到兴趣点数据，用户签到的信息使下一兴趣点推荐成为近年来研究的热点问题。下一兴趣点推荐的准确性主要受到两方面的制约：一方面，签到数据稀疏性问题。当前研究者通过引入兴趣点的地理相关性或社交网络中的朋友评价信息来改善数据稀疏问题，但并不是所有兴趣点之间都存在强地理相关性，且社交网络中只存在少量用户对签到的兴趣点发表评论。另一方面，基于深度学习训练兴趣点签到序列存在梯度消失的问题。针对这些问题，提出融合区域与朋友影响的用户下一兴趣点推荐模型。首先，将兴趣点区域信息融入用户签到兴趣点序列中；其次，使用带有残差连接的神经网络模型对序列进行嵌入，避免梯度消失，提高模型收敛性；最后，融合朋友访问的兴趣点信息进行下一兴趣点推荐，进一步提高兴趣点推荐的准确性。实验数据表明，与其他推荐模型相比，提出的模型具有较高准确性。

关键词: 兴趣点（POI）, 兴趣点推荐, 社交关系, 区域, 残差网络

ZHANG Aoya, SHI Meihui, SHEN Derong, KOU Yue, NIE Tiezheng. Integrate Influence of Regions and Friends for Next POI Recommendation[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(12): 2335-2344.

张奥雅, 石美惠, 申德荣, 寇月, 聂铁铮. 融合区域与朋友影响的下一兴趣点推荐[J]. 计算机科学与探索, 2021, 15(12): 2335-2344.

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