{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T18:05:43Z","timestamp":1791223543045,"version":"4.1.0"},"reference-count":33,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIC"],"published-print":{"date-parts":[[2023,2,9]]},"abstract":"<jats:p>Location-based social networks (LBSNs) have greatly promoted the development of the field of human mobility mining. However, the sparsity, multimodality and heterogeneity nature of the user check-in data remains a great concern for learning high-quality user or other entity representations, especially in the downstream application tasks, such as point-of-interest (POI) recommendation. Most existing methods focus on user preference modeling based on sequential POI tags without exploring the interaction between different modalities (e.g., user-user interactions, user-timestamp interactions, user-POI interactions, etc.). To this end, we introduce a multimodal interaction aware embedding framework to generate reliable entity embeddings on the heterogeneous socio-spatial network. At its core, first, multi-modal interaction sub-graph sampling techniques are designed to capture the heterogeneous contexts; then, a self-supervised contrastive learning technique is leveraged to extract intra-modality and inter-modality interactions in a light way. We conduct experiments on the next-POI recommendation tasks based on three real-world datasets. Experimental results demonstrate the superiority of our model over the state-of-the-art embedding learning algorithms.<\/jats:p>","DOI":"10.3233\/aic-220161","type":"journal-article","created":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T11:46:30Z","timestamp":1670931990000},"page":"41-55","source":"Crossref","is-referenced-by-count":3,"title":["Multimodal interaction aware embedding for location-based social networks"],"prefix":"10.1177","volume":"36","author":[{"given":"Ruiyun","family":"Yu","sequence":"first","affiliation":[{"name":"Software College, Northeastern University, Chuangxin Street, Shenyang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kang","family":"Yang","sequence":"additional","affiliation":[{"name":"Software College, Northeastern University, Chuangxin Street, Shenyang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihong","family":"Wang","sequence":"additional","affiliation":[{"name":"Software College, Northeastern University, Chuangxin Street, Shenyang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shi","family":"Zhen","sequence":"additional","affiliation":[{"name":"Software College, Northeastern University, Chuangxin Street, Shenyang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/AIC-220161_ref1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/294"},{"key":"10.3233\/AIC-220161_ref2","doi-asserted-by":"crossref","unstructured":"S.\u00a0Feng, L.V.\u00a0Tran, G.\u00a0Cong, L.\u00a0Chen, J.\u00a0Li and F.\u00a0Li, HME: A hyperbolic metric embedding approach for next-POI recommendation, in: SIGIR 2020, 2020, pp.\u00a01429\u20131438.","DOI":"10.1145\/3397271.3401049"},{"key":"10.3233\/AIC-220161_ref3","doi-asserted-by":"crossref","unstructured":"Q.\u00a0Guo, Z.\u00a0Sun, J.\u00a0Zhang and Y.-L.\u00a0Theng, An attentional recurrent neural network for personalized next location recommendation, in: AAAI 2020, Vol.\u00a034, 2020, pp.\u00a083\u201390.","DOI":"10.1609\/aaai.v34i01.5337"},{"key":"10.3233\/AIC-220161_ref4","unstructured":"W.L.\u00a0Hamilton, Z.\u00a0Ying and J.\u00a0Leskovec, Inductive representation learning on large graphs, in: NeurIPS 2017, 2017, pp.\u00a01024\u20131034."},{"key":"10.3233\/AIC-220161_ref5","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/344"},{"key":"10.3233\/AIC-220161_ref6","doi-asserted-by":"crossref","unstructured":"X.\u00a0He, K.\u00a0Deng, X.\u00a0Wang, Y.\u00a0Li, Y.\u00a0Zhang and M.\u00a0Wang, LightGCN: Simplifying and powering graph convolution network for recommendation, in: SIGIR 2020, 2020, pp.\u00a0639\u2013648.","DOI":"10.1145\/3397271.3401063"},{"key":"10.3233\/AIC-220161_ref7","doi-asserted-by":"crossref","unstructured":"W.\u00a0Huang, T.\u00a0Zhang, Y.\u00a0Rong and J.\u00a0Huang, Adaptive sampling towards fast graph representation learning, in: NeurIPS 2018, 2018, pp.\u00a04563\u20134572.","DOI":"10.1155\/2018\/5048419"},{"key":"10.3233\/AIC-220161_ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3150792"},{"key":"10.3233\/AIC-220161_ref9","unstructured":"T.N.\u00a0Kipf and M.\u00a0Welling, Semi-supervised classification with graph convolutional networks, in: ICLR 2017, 2017."},{"issue":"3","key":"10.3233\/AIC-220161_ref10","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1007\/s10707-016-0279-5","article-title":"Assessing spatiotemporal predictability of LBSN: A case study of three Foursquare datasets","volume":"22","author":"Li","year":"2018","journal-title":"GeoInformatica"},{"key":"10.3233\/AIC-220161_ref11","doi-asserted-by":"crossref","unstructured":"R.\u00a0Li, X.\u00a0Wu, X.\u00a0Wu and W.\u00a0Wang, Few-shot learning for new user recommendation in location-based social networks, in: WWW 2020, 2020, pp.\u00a02472\u20132478.","DOI":"10.1145\/3366423.3379994"},{"key":"10.3233\/AIC-220161_ref12","doi-asserted-by":"crossref","unstructured":"F.\u00a0Liu, Z.\u00a0Cheng, L.\u00a0Zhu, Z.\u00a0Gao and L.\u00a0Nie, Interest-aware message-passing GCN for recommendation, in: WWW 2021, 2021, pp.\u00a01296\u20131305.","DOI":"10.1145\/3442381.3449986"},{"key":"10.3233\/AIC-220161_ref13","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.future.2019.07.074","article-title":"GLR: A graph-based latent representation model for successive POI recommendation","volume":"102","author":"Lu","year":"2020","journal-title":"Future Gener. 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