融合区域与朋友影响的下一兴趣点推荐

doi:10.3778/j.issn.1673-9418.2009087

摘要/Abstract

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

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

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.

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.

参考文献

[1] CHENG C, YANG H Q, KING I, et al. Fused matrix factori-zation with geographical and social influence in location-based social networks[C]//Proceedings of the 26th AAAI Conference on Artificial Intelligence, Toronto, Jul 22-26, 2012. Menlo Park: AAAI, 2012: 17-23.
[2] ZHANG J D, CHOW C Y, LI Y H. LORE: exploiting sequ-ential influence for location recommendations[C]//Proceed-ings of the 22nd ACM SIGSPATIAL International Confer-ence on Advances in Geographic Information Systems, Dallas, Nov 4-7, 2014. New York: ACM, 2014: 103-112.
[3] WU C Y, AHMED A, BEUTEL A, et al. Recurrent recom-mender networks[C]//Proceedings of the 10th ACM Inter-national Conference on Web Search and Data Mining, Cam-bridge, Feb 6-10, 2017. New York: ACM, 2017: 495-503.
[4] LIU C Y, LIU J P, WANG J, et al. An attention-based spatio-temporal gated recurrent unit network for point-of-interest recommendation[J]. ISPRS International Journal of Geo-Information, 2019, 8(8): 355.
[5] ZHAO P P, ZHU H F, LIU Y C, et al. Where to go next: a spatio temporal gated network for next POI recommendation[C]//Proceedings of the 33rd AAAI Conference on Artifi-cial Intelligence, the 31st Innovative Applications of Artifi-cial Intelligence Conference, the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, Honolulu, Jan 27-Feb 1, 2019. Menlo Park: AAAI, 2019: 5877-5884.
[6] FENG S S, LI X T, ZENG Y F, et al. Personalized ranking metric embedding for next new POI recommendation[C]//Proceedings of the 24th International Joint Conference on Artificial Intelligence, Buenos Aires, Jul 25-31, 2015. Menlo Park: AAAI, 2015: 2069-2075.
[7] MIKOLOV T, KARAFIáT M, BURGET L, et al. Recurrent neural network based language model[C]//Proceedings of the 11th Annual Conference of the International Speech Com-munication Association, Makuhari, Chiba, Sep 26-30, 2010: 1045-1048.
[8] LIU Q, WU S, WANG L, et al. Predicting the next location: a recurrent model with spatial and temporal contexts[C]//Proceedings of the 30th AAAI Conference on Artificial Intelligence, Phoenix, Feb 12-17, 2016. Menlo Park: AAAI, 2016: 194-200.
[9] XU C F, ZHAO P P, LIU Y C, et al. Recurrent convolu-tional neural network for sequential recommendation[C]//Proceedings of the 2019 World Wide Web Conference, San Francisco, May 13-17, 2019. New York: ACM, 2019: 3398-3404.
[10] WANG S J, HU L, CAO L B, et al. Attention-based tran-sactional context embedding for next-item recommendation[C]//Proceedings of the 32nd AAAI Conference on Arti-ficial Intelligence, the 30th Innovative Applications of Arti-ficial Intelligence, and the 8th AAAI Symposium on Educa-tional Advances in Artificial Intelligence, New Orleans, Feb 2-7, 2018. Menlo Park: AAAI, 2018: 2532-2539.
[11] YE M, YIN P F, LEE W C, et al. Exploiting geographical influence for collaborative point-of-interest recommenda-tion[C]//Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, Beijing, Jul 25-29, 2011. New York: ACM, 2011: 325-334.
[12] XU Y, LI X, LI J, et al. SSSER: spatio-temporal sequential and social embedding rank for successive point-of-interest recommendation[J]. IEEE Access, 2019, 7: 156804-156823.
[13] PAN Z G, CUI L, WU X Y, et al. Deep potential geo-social relationship mining for point-of-interest recommendation[J]. IEEE Access, 2019, 7: 99496-99507.
[14] XIONG X, QIAO S J, HAN N, et al. Where to go: an effective point-of-interest recommendation framework for heterogeneous social networks[J]. Neurocomputing, 2020,373: 56-69.
[15] FENG S S, CONG G, AN B, et al. POI2Vec: geographical latent representation for predicting future visitors[C]//Pro-ceedings of the 31st AAAI Conference on Artificial Intelli-gence, San Francisco, Feb 4-9, 2017. Menlo Park: AAAI, 2017: 102-108.
[16] LI Y, LUO Y D, ZHANG Z, et al. Context-aware attention-based data augmentation for POI recommendation[C]//Pro-ceedings of the 35th IEEE International Conference on Data Engineering Workshops, Macao, China, Apr 8-12, 2019. Pis-cataway: IEEE, 2019: 177-184.
[17] HE K M, ZHANG X Y, REN S Q, et al. Deep residual lear-ning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recogni-tion, Las Vegas, Jun 27-30, 2016. Washington: IEEE Com-puter Society, 2016: 770-778.
[18] RENDLE S, FREUDENTHALER C, SCHMIDT-THIEME L, et al. Factorizing personalized Markov chains for next-basket recommendation[C]//Proceedings of the 19th Inter-national Conference on World Wide Web, Raleigh, Apr 26-30, 2010. New York: ACM, 2010: 811-820.