{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:49:54Z","timestamp":1760161794159,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,2,8]],"date-time":"2021-02-08T00:00:00Z","timestamp":1612742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Key Project of National Key R&amp;D Project","award":["2017YFC1703303"],"award-info":[{"award-number":["2017YFC1703303"]}]},{"name":"Natural Science Foundation of Fujian Province of China","award":["2020J01435,2019J01846"],"award-info":[{"award-number":["2020J01435,2019J01846"]}]},{"name":"External Cooperation Project of Fujian Province, China","award":["2019I0001"],"award-info":[{"award-number":["2019I0001"]}]},{"name":"Science and Technology Guiding Project of Fujian Province, China","award":["2019Y0046"],"award-info":[{"award-number":["2019Y0046"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The traditional heterogeneous embedding method based on a random walk strategy does not focus on the random walk fundamentally because of higher-order Markov chains. One of the important properties of Markov chains is stationary distributions (SDs). However, in large-scale network computation, SDs are not feasible and consume a lot of memory. So, we use a non-Markovian space strategy, i.e., a heterogeneous personalized spacey random walk strategy, to efficiently get SDs between nodes and skip some unimportant intermediate nodes, which allows for more accurate vector representation and memory savings. This heterogeneous personalized spacey random walk strategy was extended to heterogeneous space embedding methods in combination with vector learning, which is better than the traditional heterogeneous embedding methods for node classification tasks. As an excellent embedding method can obtain more accurate vector representations, it is important for the improvement of the recommendation model. In this article, recommendation algorithm research was carried out based on the heterogeneous personalized spacey embedding method. For the problem that the standard random walk strategy used to compute the stationary distribution consumes a large amount of memory, which may lead to inefficient node vector representation, we propose a meta-path-based heterogenous personalized spacey random walk for recommendation (MPHSRec). The meta-path-based heterogeneous personalized spacey random walk strategy is used to generate a meaningful sequence of nodes for network representation learning, and the learned embedded vectors of different meta-paths are transformed by a nonlinear fusion function and integrated into a matrix decomposition model for rating prediction. The experimental results demonstrate that MPHSRec not only improves the accuracy, but also reduces the memory cost compared with other excellent algorithms.<\/jats:p>","DOI":"10.3390\/sym13020290","type":"journal-article","created":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T04:33:46Z","timestamp":1612931626000},"page":"290","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Recommendation Model Based on a Heterogeneous Personalized Spacey Embedding Method"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6679-8655","authenticated-orcid":false,"given":"Qunsheng","family":"Ruan","sequence":"first","affiliation":[{"name":"School of Informatics, Xiamen University, Haiyun Park, Siming District, Xiamen 361000, China"}]},{"given":"Yiru","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Haiyun Park, Siming District, Xiamen 361000, China"}]},{"given":"Yuhui","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Haiyun Park, Siming District, Xiamen 361000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5510-3160","authenticated-orcid":false,"given":"Yingdong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Haiyun Park, Siming District, Xiamen 361000, China"}]},{"given":"Qingfeng","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Haiyun Park, Siming District, Xiamen 361000, China"}]},{"given":"Tianqi","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Haiyun Park, Siming District, Xiamen 361000, China"}]},{"given":"Xiling","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information, Mechanical and Electrical Engineering, Normal University, Ningde 352100, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sun, Y.Z., Han, J.W., Yan, X.F., Yu, P., and Wu, T.Y. (2011, January 11\u201314). PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks. Proceedings of the 37th International Conference on Very Large Data Bases (VLDB), Seattle, WA, USA.","DOI":"10.14778\/3402707.3402736"},{"key":"ref_2","unstructured":"Yu, X., Ren, X., Gu, Q., and Sun, Y. (2013, January 3\u20139). Collaborative Filtering with Entity Similarity Regularization in Heterogeneous Information Networks. Proceedings of the 23th International Joint Conference on Artificial Intelligence (IJCAI), Beijing, China."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Feng, W., and Wang, J.Y. (2012, January 12\u201316). Incorporating Heterogeneous Information for Personalized Tag Recommendation in Social Tagging Systems. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Beijing, China.","DOI":"10.1145\/2339530.2339729"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"He, Y., Song, Y.Q., Li, C., Peng, J., and Peng, H. (2019, January 3). Hetespaceywalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding. Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM), Beijing, China.","DOI":"10.1145\/3357384.3358061"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1145\/138859.138867","article-title":"Using collaborative filtering to weave an information TAPESTRY","volume":"35","author":"Goldberg","year":"1992","journal-title":"Commun. ACM"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sarwar, B., Karypis, G., and Konstan, J. (2001, January 1\u20135). Item-Based Collaborative Filtering Recommendation Algorithms. Proceedings of the 10th International Conference on World Wide Web (WWW), Hong Kong, China.","DOI":"10.1145\/371920.372071"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Koren, Y. (2008, January 24\u201327). Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model. Proceedings of the 14th ACMKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, NV, USA.","DOI":"10.1145\/1401890.1401944"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2479","DOI":"10.1109\/TKDE.2013.2297920","article-title":"Hetesim: A general framework for relevance measure in heterogeneous networks","volume":"26","author":"Shi","year":"2013","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_9","unstructured":"Shi, C., Zhang, Z., Luo, P., and Yu, P.S. (2015, January 19\u201323). CIKM\u201915-. Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM), Melbourne, Australia."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhao, H., Yao, Q.M., Li, J.D., Song, Y.Q., and Lee, D.L. (2017, January 13). Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Halifax, NS, Canada.","DOI":"10.1145\/3097983.3098063"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1049\/cje.2018.05.002","article-title":"A New Algorithm for Literature Recommendation Based on a Bibliographic Heterogeneous Information Network","volume":"27","author":"Li","year":"2018","journal-title":"Chin. J. Electron."},{"key":"ref_12","first-page":"1256","article-title":"BRScS: A Hybrid Recommendation Model Fusing Multi-source Heterogeneous data","volume":"2020","author":"Ji","year":"2020","journal-title":"EURASIP J. Wirel. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1007\/s13042-017-0778-1","article-title":"Friend recommendation in social networks based on multi-source information fusion","volume":"10","author":"Cheng","year":"2019","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2802","DOI":"10.1109\/TPDS.2020.3003307","article-title":"Distributed Training of Deep Learning Models: A Taxonomic Perspective","volume":"31","author":"Langer","year":"2020","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fan, S.H., Zhu, J.X., Han, X.T., and Shi, C. (2019, January 4\u20138). Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330673"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Song, W.P., Xiao, Z.P., and Wang, Y.F. (2019, January 30). Session-Based Social Recommendation via Dynamic Graph Attention Networks. Proceedings of the 12th ACM International Conference on Web Search and Data Mining (WSDM), Melbourne, Australia.","DOI":"10.1145\/3289600.3290989"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Fan, W., Ma, Y., Li, Q., He, Y., Zhao, E., Tang, J., and Yin, D. (2019, January 4\u20136). Graph Neural Networks for Social Recommendation. Proceedings of the World Wide Web Conference (WWW), San Francisco, CA, USA.","DOI":"10.1145\/3308558.3313488"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ji, M., Han, J.W., and Marina, D. (2011, January 23\u201327). Ranking-Based Classification of Heterogeneous Information Networks. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), San Diego, CA, USA.","DOI":"10.1145\/2020408.2020603"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Sun, Y.Z., Brandon, N., Han, J.W., Yan, X.F., and Yu, P.S. (2012, January 12\u201316). Integrating Meta-Path Selection with User-Guided Object Clustering in Heterogeneous Information Networks. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, China.","DOI":"10.1145\/2339530.2339738"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sun, Y.Z., Yu, Y.T., and Han, J.W. (2009, January 28). Ranking-based Clustering of Heterogeneous Information Networks with Star Network Schema. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France.","DOI":"10.1145\/1557019.1557107"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hu, B.B., Shi, C., and Zhao, W. (2018, January 19\u201323). Leveraging Meta-Path Based Context for Top-N Recommendation with A Neural Co-Attention Model. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3219819.3219965"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yu, X., Ren, X., Sun, Y.Z., Gu, Q.Q., Sturt, B., and Khandelwal, U. (2014, January 28). Personalized Entity Recommendation: A Heterogeneous Information Network Approach. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA.","DOI":"10.1145\/2556195.2556259"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1016\/j.ipm.2019.102074","article-title":"Fast top-k similarity search in large dynamic attributed networks","volume":"56","author":"Meng","year":"2019","journal-title":"Inf. Process. Manag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/0169-7439(87)80084-9","article-title":"Principal component analysis","volume":"2","author":"Wold","year":"1987","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kruskal, J.B., Wish, M., and Uslaner, E.M. (1978). Multidimensional Scaling, Sage.","DOI":"10.4135\/9781412985130"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., and Skiena, S. (2014, January 24). DeepWalk: Online Learning of Social Representations. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA.","DOI":"10.1145\/2623330.2623732"},{"key":"ref_27","first-page":"562","article-title":"Efficient Estimation of Word Representations in Vector Space","volume":"28","author":"Mikolov","year":"2013","journal-title":"Comput. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tang, J., Qu, M., and Wang, M. (2015, January 18). LINE: Large-Scale Information Network Embedding. Proceedings of the 24th International Conference on World Wide Web (WWW), Florence, Italy.","DOI":"10.1145\/2736277.2741093"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Dong, Y.X., Chawla, N.V., and Swami, A. (2017, January 13). Metapath2vec: Scalable Representation Learning for Heterogeneous Networks. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada.","DOI":"10.1145\/3097983.3098036"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, D., Yin, J., and Zhu, X. (2018). Metagraph2vec: Complex Semantic Path Augmented Heterogeneous Network Embedding. Advances in Knowledge Discovery and Data Mining, Springer.","DOI":"10.1007\/978-3-319-93037-4_16"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/s10994-010-5205-8","article-title":"Relational Retrieval Using a Combination of Path-constrained Random Walk","volume":"81","author":"Laok","year":"2010","journal-title":"Mach. Learn."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1007\/s10115-016-0916-1","article-title":"Constrained-meta-path-based ranking in heterogeneous information network","volume":"49","author":"Shi","year":"2016","journal-title":"Knowl. Inf. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Han, J., and Moraga, C. (1995, January 9). The Influence of the Sigmoid Function Parameters on the Speed of Backpropagation Learning. Proceedings of the International Workshop on Artificial Neural Networks, Torremolinos, Spain.","DOI":"10.1007\/3-540-59497-3_175"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.neucom.2020.08.018","article-title":"AIRec: Attentive intersection model for tag-aware recommendation","volume":"421","author":"Chen","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_35","unstructured":"Salakhutdinov, R., and Mnih, A. (2007, January 3). Probabilistic Matrix Factorization. In Proceedings of the 20th International Conference on Neural Information Processing System, Vancouver, BC, Canada."},{"key":"ref_36","unstructured":"(2019, December 12). Douban. Available online: https:\/\/book.douban.com."},{"key":"ref_37","unstructured":"(2019, December 20). Yelp. Available online: https:\/\/www.Yelp.com\/dataset-challenge."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1109\/TKDE.2018.2833443","article-title":"Heterogeneous Information Network Embedding for Recommendation","volume":"31","author":"Shi","year":"2019","journal-title":"IEEE Trans. Knowl. Data Eng."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/2\/290\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:21:33Z","timestamp":1760160093000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/2\/290"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,8]]},"references-count":38,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["sym13020290"],"URL":"https:\/\/doi.org\/10.3390\/sym13020290","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2021,2,8]]}}}