{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T10:41:25Z","timestamp":1722940885003},"reference-count":26,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T00:00:00Z","timestamp":1676419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Project of Application Foundation of Sichuan Science and Technology Department","award":["2019YJ0455","z2011089","YCJJ2021072"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"Hash is one of the most widely used methods for computing efficiency and storage efficiency. With the development of deep learning, the deep hash method shows more advantages than traditional methods. This paper proposes a method to convert entities with attribute information into embedded vectors (FPHD). The design uses the hash method to quickly extract entity features, and uses a deep neural network to learn the implicit association between entity features. This design solves two main problems in large-scale dynamic data addition: (1) The linear growth of the size of the embedded vector table and the size of the vocabulary table leads to huge memory consumption. (2) It is difficult to deal with the problem of adding new entities to the retraining model. Finally, taking the movie data as an example, this paper introduces the encoding method and the specific algorithm flow in detail, and realizes the effect of rapid reuse of dynamic addition data model. Compared with three existing embedding algorithms that can fuse entity attribute information, the deep hash embedding algorithm proposed in this paper has significantly improved in time complexity and space complexity.<\/jats:p>","DOI":"10.3390\/e25020361","type":"journal-article","created":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T11:33:01Z","timestamp":1676460781000},"page":"361","source":"Crossref","is-referenced-by-count":2,"title":["Design and Application of Deep Hash Embedding Algorithm with Fusion Entity Attribute Information"],"prefix":"10.3390","volume":"25","author":[{"given":"Xiaoli","family":"Huang","sequence":"first","affiliation":[{"name":"Research Institution of Signal Detection and Information Processing Technology, Xihua University, Chengdu 610039, China"}]},{"given":"Haibo","family":"Chen","sequence":"additional","affiliation":[{"name":"Research Institution of Signal Detection and Information Processing Technology, Xihua University, Chengdu 610039, China"}]},{"given":"Zheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Research Institution of Signal Detection and Information Processing Technology, Xihua University, Chengdu 610039, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Weinberger, K., Dasgupta, A., Langford, J., Smola, A., and Attenberg, J. (2009, January 14\u201318). Feature hashing for large scale multitask learning. Proceedings of the 26th Annual International Conference on Machine Learning, Montreal, QC, Canada.","DOI":"10.1145\/1553374.1553516"},{"key":"ref_2","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., and Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Adv. Neural Inf. Process. Syst., 26."},{"key":"ref_3","unstructured":"Uchida, S., Yoshikawa, T., and Furuhashi, T. (2020, January 5\u20138). Application of Output Embedding on Word2Vec. Proceedings of the ICASSP 2020\u20142020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toyama, Japan."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Joulin, A., Grave, E., Bojanowski, P., and Mikolov, T. (2016). Bag of tricks for efficient text classification. arXiv.","DOI":"10.18653\/v1\/E17-2068"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yao, T., Zhai, Z., and Gao, B. (2020, January 20\u201322). Text Classification Model Based on fastText. Proceedings of the International Conference on Artificial Intelligence and Information Systems (ICAIIS), Dalian, China.","DOI":"10.1109\/ICAIIS49377.2020.9194939"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Barkan, O., and Koenigstein, N. (2016, January 13\u201316). Item2vec: Neural item embedding for collaborative filtering. Proceedings of the IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), Vietri sul Mare, Italy.","DOI":"10.1109\/MLSP.2016.7738886"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Barkan, O., Caciularu, A., Katz, O., and Koenigstein, N. (2020, January 4\u20138). Attentive Item2vec: Neural Attentive User Representations. Proceedings of the ICASSP 2020\u20142020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (ICAIIS), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053071"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Covington, P., Adams, J., and Sargin, E. (2016, January 15\u201319). Deep neural networks for youtube recommendations. Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA.","DOI":"10.1145\/2959100.2959190"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Cheng, H.T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., and Shah, H. (2016, January 15). Wide & deep learning for recommender systems. Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, 10th ACM Conference on Recommender Systems, Boston, MA, USA.","DOI":"10.1145\/2988450.2988454"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kang, W.C., Cheng, D.Z., Yao, T., Yi, X., Chen, T., Hong, L., and Chi, E.H. (2020). Learning to embed categorical features without embedding tables for recommendation. arXiv.","DOI":"10.1145\/3447548.3467304"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., and Skiena, S. (2014, January 24\u201327). 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_12","unstructured":"Hamilton, W., Ying, Z., and Leskovec, J. (2017, January 4\u20139). Inductive representation learning on large graphs. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_13","first-page":"399","article-title":"Fast and accurate network embeddings via very sparse random projection","volume":"66","author":"Chen","year":"2019","journal-title":"ACM Int. Conf. Inf. Knowl. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1016\/S0022-0000(03)00025-4","article-title":"Database-friendly random projections: Johnson-Lindenstrauss with binary coins","volume":"66","author":"Achlioptas","year":"2003","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_15","unstructured":"Argerich, L., Zaffaroni, J.T., and Cano, M.J. (2016). Hash2vec, feature hashing for word embeddings. arXiv."},{"key":"ref_16","first-page":"4928","article-title":"Hash embeddings for efficient word representations","volume":"30","author":"Svenstrup","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_17","unstructured":"Luo, X., Wang, H., Wu, D., Chen, C., Deng, M., Huang, J., and Hua, X.S. (2020). A survey on deep hashing methods. ACM Trans. Knowl. Discov. Data (TKDD)."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yan, B., Wang, P., Liu, J., Lin, W., Lee, K.C., Xu, J., and Zheng, B. (2021, January 1\u20135). Binary code based hash embedding for web-scale applications. Proceedings of the 30th ACM International Conference on Information & Knowledge Management, Virtual Event, QLD, Australia.","DOI":"10.1145\/3459637.3482065"},{"key":"ref_19","unstructured":"Al-Ansari, K. (2022, November 28). Survey on Word Embedding Techniques in Natural Language Processing. Available online: https:\/\/www.researchgate.net\/publication\/343686323_Survey_on_Word_Embedding_Techniques_in_Natural_Language_Processing."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Deng, Y. (2021). Recommender systems based on graph embedding techniques: A comprehensive review. arXiv.","DOI":"10.1109\/ACCESS.2022.3174197"},{"key":"ref_21","first-page":"24695","article-title":"Rot-pro: Modeling transitivity by projection in knowledge graph embedding","volume":"34","author":"Song","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Shi, H.M., Mudigere, D., Naumov, M., and Yang, J. (2020, January 6\u201310). Compositional embeddings using complementary partitions for memory-efficient recommendation systems. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event, CA, USA.","DOI":"10.1145\/3394486.3403059"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"042178","DOI":"10.1088\/1755-1315\/267\/4\/042178","article-title":"The research of BP neural network based on one-hot encoding and principle component analysis in determining the therapeutic effect of diabetes mellitus","volume":"267","author":"Qiao","year":"2019","journal-title":"Iop Conf. Ser. Earth Environ. Sci. Iop Publ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7354081","DOI":"10.1155\/2018\/7354081","article-title":"Deep convolutional neural network based ECG classification system using information fusion and one-hot encoding techniques","volume":"2018","author":"Li","year":"2018","journal-title":"Math. Probl. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, F., Yan, B., Long, Q., Wang, P., Lin, W., Xu, J., and Zheng, B. (2021). Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction. arXiv.","DOI":"10.1145\/3404835.3463015"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Grover, A., and Leskovec, J. (2016, January 13\u201317). node2vec: Scalable feature learning for networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939754"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/2\/361\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T05:16:28Z","timestamp":1677042988000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/2\/361"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,15]]},"references-count":26,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["e25020361"],"URL":"https:\/\/doi.org\/10.3390\/e25020361","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints202212.0040.v1","asserted-by":"object"}]},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,15]]}}}