Point-of-interest recommendation based on LBSN with multi-aspect fusion of social and individual features
Authors: 
Yishan Zhang, Yu LiuAuthors Info & Claims
Yishan Zhang, Yu LiuAuthors Info & ClaimsPages 12163 - 12184
Abstract
Point-of-interest (POI) recommendation is of paramount importance to user travel efficiency since users always expect their intended POIs can be recommended within a specified geospatial range. However, existing methods may not well establish user–user, user–POI, and POI–POI relatedness in a feature-interaction manner and social influence may not be infused into the recommendation process through feature fusion, causing unstable recommendation accuracy in different real-world datasets with multiple variables. A multi-aspect fusion of social and individual features POI recommendation method is proposed in this study, establishing feature interaction gate with user check-in ability, POI popularity, and POI physical characteristics involved. Furthermore, these multi-aspect feature interactions are exploited to incorporate multimodal data and establish information sharing and delivery between users in internal fusion through embedded factorization machine variants imposing individual influence and social influence in location-based social network (LBSN) on recommendation results. Moreover, relatedness enhancement module is established to balance contextual influence and social influence on user next movement decision in external fusion such that direct external information can be transmitted and shared, which diversifies recommendation results. Extensive experiments are conducted on two real-world datasets, and the results show that the proposed model achieves significantly superiority compared with its state-of-the-art baseline models and effectiveness of each proposed modules.
References
[1]
Li X, Hu R, and Wang Z Beyond fixed time and space: next POI recommendation via multi-grained context and correlation Neural Comput Appl 2023 35 1 907-920
[2]
Thaipisutikul T and Shih TK A novel context-aware recommender system based on a deep sequential learning approach (CReS) Neural Comput Appl 2021 33 11067-11090
[3]
Logesh R, Subramaniyaswamy V, Vijayakumar V, Gao X, and Wang G Hybrid bio-inspired user clustering for the generation of diversified recommendations Neural Comput Appl 2020 32 2487-2506
[4]
Logesh R, Subramaniyaswamy V, Malathi D, Sivaramakrishnan N, and Vijayakumar N Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method Neural Comput Appl 2020 32 2141-2164
[5]
Cepeda-Pacheco JC and Domingo MC Deep learning and internet of things for tourist attraction recommendations in smart cities Neural Comput Appl 2022 34 10 7691-7709
[6]
Ke S, Xie M, Zhu H, and Cao Z Group-based recurrent neural network for human mobility prediction Neural Comput Appl 2022 34 12 9863-9883
[7]
Zhao P, Luo A, Liu Y, Xu J, Li Z, Zhuang F, and Zhou X Where to go next: a spatio-temporal gated network for next poi recommendation IEEE Trans Knowl Data Eng 2020 34 5 2512-2524
[8]
Zhu S, Wang D, Liu L, Wang Y, and Guo D Inferring region significance by using multi-source spatial data Neural Comput Appl 2020 32 6523-6531
[9]
Islam MA, Mohammad MM, Das SSS, and Ali ME A survey on deep learning based point-of-interest (POI) recommendations Neurocomputing 2022 472 306-325
[10]
Xu Y, Wang Z, and Shang JS PAENL: personalized attraction enhanced network learning for recommendation Neural Comput Appl 2021 6 1-11
[11]
Davtalab M and Alesheikh AA A POI recommendation approach integrating social spatio-temporal information into probabilistic matrix factorization Knowl Inf Syst 2021 63 65-85
[12]
Zhang B, Wang Q, Li J, and Ye Z Spatial–temporal grid clustering method based on frequent stay point recognition Neural Comput Appl 2022 8 1-9
[13]
Sun K, Qian T, Chen T, Liang Y, Nguyen QVH, Yin H (2020) Where to go next: modeling long-and short-term user preferences for point-of-interest recommendation. In: Proceedings of the AAAI conference on artificial intelligence. AAAI, pp 214–221
[14]
Hu B, Ye Y, Zhong Y, Pan J, and Hu M Transmkr: translation-based knowledge graph enhanced multi-task point-of-interest recommendation Neurocomputing 2022 474 107-114
[15]
Liu K, Zheng W, Xiao Y, Zhai X (2022) Poi recommendation algorithm based on region transfer collaborative filtering. In: Proceedings of the IEEE 25th international conference on computer supported cooperative work in design. IEEE, pp 903–907
[16]
Zeng J, Tang H, Zhao Y, Gao M, and Wen J Pr-rcuc: a poi recommendation model using region-based collaborative filtering and user-based mobile context Mob Netw Appl 2021 26 6 2434-2444
[17]
Wan L, Wang H, Hong Y, Li R, Chen W, and Huang Z iTourSPOT: a context-aware framework for next POI recommendation in location-based social networks Int J Digit Earth 2022 15 1 1614-1636
[18]
Wang F, Qu Y, Zheng L, Lu CT, Philip SY (2017) Deep and broad learning on content-aware POI recommendation. In: Proceedings of the 3rd international conference on collaboration and internet computing. IEEE, pp 369–378
[19]
Liu S and Wang L A self-adaptive point-of-interest recommendation algorithm based on a multi-order Markov model Futur Gener Comput Syst 2018 89 506-514
[20]
Zhang Y, Shi Z, Zuo W, Yue L, Liang S, and Li X Joint personalized Markov Chains with social network embedding for cold-start recommendation Neurocomputing 2020 386 208-220
[21]
Cho K, Van Merriënboer B, Bahdanau D, Bengio Y (2014) On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259
[22]
Zhang J, Liu X, Zhou X, and Chu X Leveraging graph neural networks for point-of-interest recommendations Neurocomputing 2021 462 1-13
[23]
Li X, Xu S, Jiang T, Wang Y, Ma Y, and Liu Y POI recommendation method of neural matrix factorization integrating auxiliary attribute information Mathematics 2022 10 19 3411
[24]
Hochreiter S and Schmidhuber J Long short-term memory Neural Comput 1997 9 8 1735-1780
[25]
Karagiannis I, Arampatzis A, Efraimidis PS, Stamatelatos G (2015) Social network analysis of public lists of POIs. In: Proceedings of the 19th panhellenic conference on informatics, pp 61–62
[26]
Xu Z, Cui G, Zhong M, and Wang X Anomalous urban mobility pattern detection based on GPS trajectories and POI data ISPRS Int J Geo Inf 2019 8 7 308
[27]
Tourinho IADS and Rios TN FACF: fuzzy areas-based collaborative filtering for point-of-interest recommendation Int J Comput Sci Eng 2021 24 1 27-41
[28]
Liu B, Xiong H, Papadimitriou S, Fu Y, and Yao Z A general geographical probabilistic factor model for point of interest recommendation IEEE Trans Knowl Data Eng 2014 27 5 1167-1179
[29]
Halder S, Lim KH, Chan J, and Zhang X POI recommendation with queuing time and user interest awareness Data Min Knowl Disc 2022 36 6 2379-2409
[30]
Han P, Shang S, Sun A, Zhao P, Zheng K, and Zhang X Point-of-interest recommendation with global and local context IEEE Trans Knowl Data Eng 2021 34 11 5484-5495
[31]
Rendle S (2010) Factorization machines. In: Proceedings of the 2010 IEEE international conference on data mining. IEEE, pp 995–1000
[32]
Khatri S, Vachhani H, Shah S, Bhatia J, Chaturvedi M, Tanwar S, and Kumar N Machine learning models and techniques for VANET based traffic management: implementation issues and challenges Peer-to-Peer Network Appl 2021 14 1778-1805
[33]
Yang B, Chen J, Kang Z, and Li D Memory-aware gated factorization machine for top-N recommendation Knowl Based Syst 2020 201 106048
[34]
Huang J, Tong Z, and Feng Z Geographical POI recommendation for internet of things: a federated learning approach using matrix factorization Int J Commun Syst 2022 8 e5161
[35]
Sang Y, Sun H, Li C, and Yin L LSVP: A visual based deep neural direction learning model for point-of-interest recommendation on sparse check-in data Neurocomputing 2021 446 204-210
[36]
Jallouli M, Lajmi S, and Amous I When contextual information meets recommender systems: extended SVD++ models Int J Comput Appl 2022 44 4 349-356
[37]
Tian J, Chang M, Ding Z, Han X, & Chen Y (2022) MAHGE: point-of-interest recommendation using meta-path aggregated heterogeneous graph embeddings. In: Proceedings of the international conference on spatial data and intelligence. Cham, pp 250–263
[38]
Wang X, Liu X, Li L, Chen X, Liu J, Wu H (2021) Time-aware user modeling with check-in time prediction for next POI recommendation. In: Proceedings of the 2021 IEEE international conference on web services (ICWS). IEEE, pp 125–134
[39]
Cui Y, Sun H, Zhao Y, Yin H, and Zheng K Sequential-knowledge-aware next POI recommendation: a meta-learning approach ACM Trans Inf Syst 2021 40 2 1-22
[40]
Chen T, Yin H, Nguyen QVH, Peng W, Li X, Zhou X (2020) Sequence-aware factorization machines for temporal predictive analytics. In: Proceedings of the 36th international conference on data engineering (ICDE). IEEE, pp 1405–1416
[41]
Zhang Z, Dong M, Ota K, Zhang Y, and Kudo Y Context-enhanced probabilistic diffusion for urban point-of-interest recommendation IEEE Trans Serv Comput 2021 15 3156-3169
[42]
Zaremba W, Sutskever I, Vinyals O (2014) Recurrent neural network regularization. arXiv preprint arXiv:1409.2329
[43]
Huang L, Ma Y, Wang S, and Liu Y An attention-based spatiotemporal lstm network for next poi recommendation IEEE Trans Serv Comput 2019 14 6 1585-1597
[44]
Zhang Z, Li C, Wu Z, Sun A, Ye D, and Luo X Next: a neural network framework for next poi recommendation Front Comp Sci 2020 14 314-333
[45]
Wang M, Lu Y, Huang J (2019) SPENT: a successive POI recommendation method using similarity-based POI embedding and recurrent neural network with temporal influence. In: Proceedings of the 2019 IEEE International conference on big data and smart computing (BigComp). IEEE, pp 1–8
[46]
Li G, Chen Q, Zheng B, Yin H, Nguyen QVH, and Zhou X Group-based recurrent neural networks for POI recommendation ACM Trans Data Sci 2020 1 1 1-18
[47]
Vinodha R and Parvathi R Content and location based point-of-interest recommendation system using HITS algorithm Internat J Uncertain Fuzziness Knowl Based Syst 2023 31 31-45
[48]
Werneck H, Silva N, Pereira A, Carvalho M, Bellogín A, Martinez-Gil J, and Rocha L A reproducible POI recommendation framework: Works mapping and benchmark evaluation Inf Syst 2022 108 102019
[49]
Bag S, Kumar SK, and Tiwari MK An efficient recommendation generation using relevant Jaccard similarity Inf Sci 2019 483 53-64
[50]
Arroyo-Fernández I, Méndez-Cruz C-F, Sierra G, Torres-Moreno J-M, and Sidorov G Unsupervised sentence representations as word information series: revisiting TF–IDF Comput Speech Lang 2019 56 107-129
[51]
Sundaram V, Ahmed S, Muqtadeer SA & Reddy RR (2021) Emotion analysis in text using TF-IDF. In: Proceedings of the 2021 11th international conference on cloud computing, data science & engineering. IEEE, pp 292–297
[52]
Alhijawi B, Fraihat S, and Awajan A Multi-factor ranking method for trading-off accuracy, diversity, novelty, and coverage of recommender systems Int J Inf Technol 2023 15 3 1427-1433
[53]
Shi M, Shen D, Kou Y, Nie T, and Yu G Attentional memory network with correlation-based embedding for time-aware POI recommendation Knowl Based Syst 2021 214 106747
[54]
Zhang L, Sun Z, Zhang J, Lei Y, Li C, Wu Z, Klanner F (2021) An interactive multi-task learning framework for next POI recommendation with uncertain check-ins. In: Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence, pp 3551–3557
[55]
Chen L, Ying Y, Lyu D, Yu S, and Chen G A multi-task embedding based personalized POI recommendation method CCF Trans Pervasive Comput Interact 2021 3 253-269
[56]
Jiang S, He W, Cui L, Xu Y, and Liu L Modeling long-and short-term user preferences via self-supervised learning for next POI recommendation ACM Trans Knowl Discov Data 2023 17 1-20
[57]
Li K, Wei H, He X, and Tian Z Relational POI recommendation model combined with geographic information PLoS ONE 2022 17 4 e0266340
[58]
Su Y, Li X, Tang W, Zha D, Xiang J, Gao N (2019) Personalized point-of-interest recommendation on ranking with poisson factorization. In: Proceedings of the 2019 international joint conference on neural networks (IJCNN). IEEE, pp 1–8
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Published: 19 April 2024
Accepted: 25 March 2024
Received: 11 August 2023
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