{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T08:32:49Z","timestamp":1761294769528,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T00:00:00Z","timestamp":1724630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Leading Talents of Science and Technology Innovation","award":["2020R52042","2021C01189","82011530399","LGG20F00015","LGG22F020010","2019FR070"],"award-info":[{"award-number":["2020R52042","2021C01189","82011530399","LGG20F00015","LGG22F020010","2019FR070"]}]},{"name":"Key Research and Development Program of Zhejiang","award":["2020R52042","2021C01189","82011530399","LGG20F00015","LGG22F020010","2019FR070"],"award-info":[{"award-number":["2020R52042","2021C01189","82011530399","LGG20F00015","LGG22F020010","2019FR070"]}]},{"name":"National Natural Science Foundation of China","award":["2020R52042","2021C01189","82011530399","LGG20F00015","LGG22F020010","2019FR070"],"award-info":[{"award-number":["2020R52042","2021C01189","82011530399","LGG20F00015","LGG22F020010","2019FR070"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["2020R52042","2021C01189","82011530399","LGG20F00015","LGG22F020010","2019FR070"],"award-info":[{"award-number":["2020R52042","2021C01189","82011530399","LGG20F00015","LGG22F020010","2019FR070"]}]},{"name":"Natural Science Foundation of Zhejiang Province","award":["2020R52042","2021C01189","82011530399","LGG20F00015","LGG22F020010","2019FR070"],"award-info":[{"award-number":["2020R52042","2021C01189","82011530399","LGG20F00015","LGG22F020010","2019FR070"]}]},{"name":"Research and Development Fund Talent Startup Project of Zhejiang A&amp;F University","award":["2020R52042","2021C01189","82011530399","LGG20F00015","LGG22F020010","2019FR070"],"award-info":[{"award-number":["2020R52042","2021C01189","82011530399","LGG20F00015","LGG22F020010","2019FR070"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The cold-start problem in sequence recommendations presents a critical and challenging issue for portable sensing devices. Existing content-aware approaches often struggle to effectively distinguish the relative importance of content features and typically lack generalizability when processing new data. To address these limitations, we propose a content-aware few-shot meta-learning (CFSM) model to enhance the accuracy of cold-start sequence recommendations. Our model incorporates a double-tower network (DT-Net) that learns user and item representations through a meta-encoder and a mutual attention encoder, effectively mitigating the impact of noisy data on auxiliary information. By framing the cold-start problem as few-shot meta-learning, we employ a model-agnostic meta-optimization strategy to train the model across a variety of tasks during the meta-learning phase. Extensive experiments conducted on three real-world datasets\u2014ShortVideos, MovieLens, and Book-Crossing\u2014demonstrate the superiority of our model in cold-start recommendation scenarios. Compared to MetaCs-DNN, the second-best approach, CFSM, achieves improvements of 1.55%, 1.34%, and 2.42% under the AUC metric on the three datasets, respectively.<\/jats:p>","DOI":"10.3390\/s24175510","type":"journal-article","created":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T03:32:01Z","timestamp":1724643121000},"page":"5510","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Content-Aware Few-Shot Meta-Learning for Cold-Start Recommendation on Portable Sensing Devices"],"prefix":"10.3390","volume":"24","author":[{"given":"Xiaomin","family":"Lv","sequence":"first","affiliation":[{"name":"School of Information Technology, The Zhejiang Shuren University, Hangzhou 310015, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Science, The Zhejiang A&F University, Hangzhou 311300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0520-7807","authenticated-orcid":false,"given":"Tongcun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Science, The Zhejiang A&F University, Hangzhou 311300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gao, J., Lin, Y., Wang, Y., Wang, X., Yang, Z., He, Y., and Chu, X. (2020, January 19\u201323). Set-sequence-graph: A multi-view approach towards exploiting reviews for recommendation. Proceedings of the 29th ACM International Conference on Information & Knowledge Management, Virtual Event, Ireland.","DOI":"10.1145\/3340531.3411939"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ying, H., Zhuang, F., Zhang, F., Liu, Y., Xu, G., Xie, X., Xiong, H., and Wu, J. (2018, January 13\u201319). Sequential recommender system based on hierarchical attention network. Proceedings of the IJCAI International Joint Conference on Artificial Intelligence, Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/546"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1109\/TNNLS.2022.3177611","article-title":"Dynamic and static representation learning network for recommendation","volume":"35","author":"Liu","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Pan, X., Chen, Y., Tian, C., Lin, Z., Wang, J., Hu, H., and Zhao, W.X. (2022, January 17\u201321). Multimodal meta-learning for cold-start sequential recommendation. Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA.","DOI":"10.1145\/3511808.3557101"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Han, J., Ma, Y., Mei, Q., and Liu, X. (2021, January 19\u201323). DeepRec: On-device deep learning for privacy-preserving sequential recommendation in mobile commerce. Proceedings of the Web Conference 2021, Ljubljana, Slovenia.","DOI":"10.1145\/3442381.3449942"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"111001","DOI":"10.1016\/j.knosys.2023.111001","article-title":"Semantic-enhanced contrastive learning for session-based recommendation","volume":"280","author":"Liu","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, S., Wang, Y., Tang, J., Shu, K., Ranganath, S., and Liu, H. (2017, January 3\u20137). What your images reveal: Exploiting visual contents for point-of-interest recommendation. Proceedings of the 26th International Conference on World Wide Web, Perth, Australia.","DOI":"10.1145\/3038912.3052638"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1109\/TKDE.2015.2508816","article-title":"Connecting social media to e-commerce: Cold-start product recommendation using microblogging information","volume":"28","author":"Zhao","year":"2015","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1007\/s11280-021-00865-8","article-title":"An integrated model based on deep multimodal and rank learning for point-of-interest recommendation","volume":"24","author":"Liao","year":"2021","journal-title":"World Wide Web"},{"key":"ref_10","unstructured":"Volkovs, M., Yu, G., and Poutanen, T. (2017, January 4\u20139). Dropoutnet: Addressing cold start in recommender systems. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_11","unstructured":"Finn, C., Abbeel, P., and Levine, S. (2017, January 6\u201311). Model-agnostic meta-learning for fast adaptation of deep networks. Proceedings of the International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lu, Y., Fang, Y., and Shi, C. (2020, January 6\u201310). Meta-learning on heterogeneous information networks for cold-start recommendation. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event, CA, USA.","DOI":"10.1145\/3394486.3403207"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bharadhwaj, H. (2019, January 14\u201319). Meta-learning for user cold-start recommendation. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8852100"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tan, H., Yao, D., Huang, T., Wang, B., Jing, Q., and Bi, J. (2021, January 15\u201318). Meta-learning enhanced neural ODE for citywide next POI recommendation. Proceedings of the 2021 22nd IEEE International Conference on Mobile Data Management (MDM), Toronto, ON, Canada.","DOI":"10.1109\/MDM52706.2021.00023"},{"key":"ref_15","unstructured":"Vartak, M., Thiagarajan, A., Miranda, C., Bratman, J., and Larochelle, H. (2017, January 4\u20139). A meta-learning perspective on cold-start recommendations for items. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lee, H., Im, J., Jang, S., Cho, H., and Chung, S. (2019, January 4\u20138). Melu: Meta-learned user preference estimator for cold-start recommendation. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330859"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Dong, M., Yuan, F., Yao, L., Xu, X., and Zhu, L. (2020, January 6\u201310). Mamo: Memory-augmented meta-optimization for cold-start recommendation. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event, CA, USA.","DOI":"10.1145\/3394486.3403113"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Liu, S., Li, Z., and Wu, S. (2021, January 2\u20139). Cold-start sequential recommendation via meta learner. Proceedings of the AAAI Conference on Artificial Intelligence, Virtually.","DOI":"10.1609\/aaai.v35i5.16601"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1109\/TKDE.2018.2885520","article-title":"Personalized video recommendation using rich contents from videos","volume":"32","author":"Du","year":"2018","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yin, H., Huang, Z., Du, X., Yang, G., and Lian, D. (2018, January 5\u20139). Discrete deep learning for fast content-aware recommendation. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, Los Angeles, CA, USA.","DOI":"10.1145\/3159652.3159688"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, J., Lu, K., Huang, Z., and Shen, H.T. (2017, January 23\u201327). Two birds one stone: On both cold-start and long-tail recommendation. Proceedings of the 25th ACM International Conference on Multimedia, Mountain View, CA, USA.","DOI":"10.1145\/3123266.3123316"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xie, M., Yin, H., Wang, H., Xu, F., Chen, W., and Wang, S. (2016, January 24\u201328). Learning graph-based poi embedding for location-based recommendation. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, Indianapolis, IN, USA.","DOI":"10.1145\/2983323.2983711"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, N., and Yeung, D.Y. (2015, January 10\u201313). Collaborative deep learning for recommender systems. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia.","DOI":"10.1145\/2783258.2783273"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kim, D., Park, C., Oh, J., Lee, S., and Yu, H. (2016, January 15\u201319). Convolutional matrix factorization for document context-aware recommendation. Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA.","DOI":"10.1145\/2959100.2959165"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, F., Yuan, N.J., Lian, D., Xie, X., and Ma, W.Y. (2016, January 13\u201317). Collaborative knowledge base embedding for recommender systems. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939673"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1723","DOI":"10.1007\/s10115-019-01396-5","article-title":"CDLFM: Cross-domain recommendation for cold-start users via latent feature mapping","volume":"62","author":"Wang","year":"2020","journal-title":"Knowl. Inf. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xu, X., Dong, H., Qi, L., Zhang, X., Xiang, H., Xia, X., Xu, Y., and Dou, W. (2024, January 14\u201318). Cmclrec: Cross-modal contrastive learning for user cold-start sequential recommendation. Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, USA.","DOI":"10.1145\/3626772.3657839"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhao, C., Li, C., Xiao, R., Deng, H., and Sun, A. (2020, January 25\u201330). CATN: Cross-domain recommendation for cold-start users via aspect transfer network. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Xi\u2019an, China.","DOI":"10.1145\/3397271.3401169"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Tay, Y., Luu, A.T., and Hui, S.C. (2018, January 19\u201323). Multi-pointer co-attention networks for recommendation. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK.","DOI":"10.1145\/3219819.3220086"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, J., Jing, M., Lu, K., Zhu, L., Yang, Y., and Huang, Z. (2019, January 27). From zero-shot learning to cold-start recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA.","DOI":"10.1609\/aaai.v33i01.33014189"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, C., Zhu, Y., Sun, A., Wang, Z., and Wang, K. (2023, January 23\u201327). A Preference Learning Decoupling Framework for User Cold-Start Recommendation. Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, Taipei, Taiwan.","DOI":"10.1145\/3539618.3591627"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Barkan, O., and Koenigstein, N. (2016, January 13\u201316). Item2vec: Neural item embedding for collaborative filtering. Proceedings of the 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), Salerno, Italy.","DOI":"10.1109\/MLSP.2016.7738886"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chang, B., Park, Y., Park, D., Kim, S., and Kang, J. (2018, January 13\u201319). Content-aware hierarchical point-of-interest embedding model for successive poi recommendation. Proceedings of the IJCAI, Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/458"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wang, X., and Wang, Y. (2014, January 7). Improving content-based and hybrid music recommendation using deep learning. Proceedings of the 22nd ACM International Conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654940"},{"key":"ref_35","unstructured":"Diederik, P.K. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_36","unstructured":"Mnih, A., and Salakhutdinov, R.R. (2007, January 3\u20136). Probabilistic matrix factorization. Proceedings of the 20th International Conference on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"He, X., and Chua, T.S. (2017, January 7\u201311). Neural factorization machines for sparse predictive analytics. Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval, Shinjuku, Japan.","DOI":"10.1145\/3077136.3080777"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Guo, H., Tang, R., Ye, Y., Li, Z., and He, X. (2017). DeepFM: A factorization-machine based neural network for CTR prediction. arXiv.","DOI":"10.24963\/ijcai.2017\/239"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yi, X., Yang, J., Hong, L., Cheng, D.Z., Heldt, L., Kumthekar, A., Zhao, Z., Wei, L., and Chi, E. (2019, January 16\u201320). Sampling-bias-corrected neural modeling for large corpus item recommendations. Proceedings of the 13th ACM Conference on Recommender Systems, Copenhagen, Denmark.","DOI":"10.1145\/3298689.3346996"},{"key":"ref_40","unstructured":"Xie, R., Zhang, S., Wang, R., Xia, F., and Lin, L. (2021, January 2\u20139). Hierarchical reinforcement learning for integrated recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, Virtually."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, K., Qian, H., Cui, Q., Liu, Q., Li, L., Zhou, J., Ma, J., and Chen, E. (2021, January 8\u201312). Multi-interactive attention network for fine-grained feature learning in ctr prediction. Proceedings of the 14th ACM International Conference on Web Search and Data Mining, Virtual Event, Israel.","DOI":"10.1145\/3437963.3441761"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/17\/5510\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:42:42Z","timestamp":1760110962000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/17\/5510"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,26]]},"references-count":41,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["s24175510"],"URL":"https:\/\/doi.org\/10.3390\/s24175510","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2024,8,26]]}}}