[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117796-en":3,"doc-seo-117796-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117796,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","A unified approach to designing sequence-based personalized food recommendation systems - tackling dynamic user behaviors","Food recommender systems rely on information filtering and machine learning to suggest items from historical user–item interactions. Many existing methods assume static interaction patterns, while real deployments involve dynamic user behaviors that evolve over time. This work introduces a sequence-based recommendation model for food scenarios, using an LSTM network to capture ordered interaction signals and a collaborative filtering component to enable personalized recommendations. Experiments on a real-world food dataset show improved precision, recall, mean average precision, and mean reciprocal rank compared with several popular approaches.","International Journal of Machine Learning and Cybernetics [https://doi.org/10.1007/s13042-023-01808-7](https://doi.org/10.1007/s13042-023-01808-7)  \nA unified approach to designing sequence‑based personalized food recommendation systems: tackling dynamic user behaviors  \nJieyu Zhang1,2 · Zidong Wang2 · Weibo Liu2 · Xiaohui Liu2 · Qiusheng Zheng1  \nReceived: 23 November 2022 / Accepted: 16 February 2023 © The Author(s) 2023  \nAbstract  \nThe recommender system (RS) is a well-known practical application of the state-of-the-art information filtering and machine learning technologies. Traditional recommendation approaches, including collaborative and content-based filtering techniques, have been widely employed to provide suggestions in RSs, where the user-item interaction matrix is the primary data source. In many application domains, interactions between users and items are more likely to be dynamic rather than static, and thus dynamic user behaviors should be taken into account when solving recommendation tasks in order to provide more accurate suggestions. In this work, we consider the sequentially ordered information from user-item interactions in the RSs where a sequence-based recommendation model is put forward with applications to the food recommendation scenario. Furthermore, the long short-term memory (LSTM) network is employed as the building block to establish such a recommendation model, and a collaborative filtering unit is adopted to make personalized food recommendation. The proposed LSTM-based RS is successfully applied to a real-world food recommendation data set. Experimental results demonstrate that the developed method outperforms some currently popular RSs in terms of precision, recall, mean average precision and mean reciprocal rank in food recommendation.  \nKeywords Food recommendation · Sequential prediction · Long-short term memory network · Collaborative filtering  \nThis work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany.  \n* Zidong Wang [Zidong.Wang@brunel.ac.uk](Zidong.Wang@brunel.ac.uk)  \nJieyu Zhang  \n[jieyu.zhang@brunel.ac.uk](jieyu.zhang@brunel.ac.uk)  \nWeibo Liu  \n[Weibo.Liu2@brunel.ac.uk](Weibo.Liu2@brunel.ac.uk)  \nXiaohui Liu  \n[Xiaohui.Liu@brunel.ac.uk](Xiaohui.Liu@brunel.ac.uk)  \nQiusheng Zheng  \n[zqszut@163.com](zqszut@163.com)  \n1 Frontier Information Technology Research Institute, Zhongyuan University of Technology, Zhengzhou 450007, China  \n2 Department of Computer Science, Brunel University London, Uxbridge, Middlesex UB8 3PH, UK  \n1 Introduction  \nFood has always been at the heart of human life. In the past, people had to identify and store food to survive, while in nowadays, people have more concerns about dietary needs including essential nutrition, health, taste, calories, and social occasions [8, 16] . Due to the growing information overload of various food-related content on multimedia, food recommender systems (RSs) are becoming increasingly attractive for people worldwide. Clearly, long-term unhealthy eating habits would be harmful to people’s health with potential risks such as the development of undesired chronic diseases. Taking into consideration of the importance of healthy eating habits, the RS is now used as an efficient tool by people to make informed decisions on food selection according to their health conditions, thereby helping people develop heathy eating habits and reduce unaware health risks [32, 54–56] .  \nGenerally speaking, RSs have the advantage of saving time and money by using a series of algorithms to analyze users’ food behaviors and ratings so as to recommend the most relevant and appealing foods to users [31] . Note that  \n1 3  \nthere are still several challenges (e.g., diversity, adaptation and fluctuation) that hinder the further development and application of RSs. The diversity challenge lies in the fact that food RSs are","cbCaibPYyDESa6qm","https://ap.wps.com/l/cbCaibPYyDESa6qm","pdf",900568,1,10,"English","en",105,"# Introduction\n## Motivation: food recommenders and health-aware selection\n## Challenges in food recommendation (diversity, adaptation, fluctuation)\n## Background: collaborative filtering and content-based filtering\n## Proposed direction: modeling dynamic user-item interaction sequences","[{\"question\":\"Why are dynamic user behaviors important for food recommendation?\",\"answer\":\"Because user-item interactions in practice change over time rather than staying static, capturing sequential dynamics can improve the accuracy of future food preference predictions and recommendations.\"},{\"question\":\"What model is proposed for the sequence-based food recommender?\",\"answer\":\"The approach uses an LSTM network to model the ordered sequence of user-item interactions, combined with a collaborative filtering unit for personalized recommendations.\"},{\"question\":\"How does the method perform on a real-world food dataset?\",\"answer\":\"Experimental results indicate the LSTM-based recommender outperforms several popular recommendation systems using metrics such as precision, recall, mean average precision, and mean reciprocal rank.\"}]","A unified approach to designing sequence-based personalized food recommendation systems - tackling dynamic user behaviors | PDF",1785679623,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-unified-approach-to-designing-sequence-based-personalized-food-recommendation-systems-tackling-dynamic-user-behaviors","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-unified-approach-to-designing-sequence-based-personalized-food-recommendation-systems-tackling-dynamic-user-behaviors/117796/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are dynamic user behaviors important for food recommendation?","Question",{"text":75,"@type":76},"Because user-item interactions in practice change over time rather than staying static, capturing sequential dynamics can improve the accuracy of future food preference predictions and recommendations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What model is proposed for the sequence-based food recommender?",{"text":80,"@type":76},"The approach uses an LSTM network to model the ordered sequence of user-item interactions, combined with a collaborative filtering unit for personalized recommendations.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the method perform on a real-world food dataset?",{"text":84,"@type":76},"Experimental results indicate the LSTM-based recommender outperforms several popular recommendation systems using metrics such as precision, recall, mean average precision, and mean reciprocal rank.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]