个性化推荐算法对用户决策行为影响研究综述

doi:10.11896/jsjkx.241100086

摘要/Abstract

摘要： 互联网在快速发展中产生了海量数据,信息过载现象也因此日益凸显。为了帮助用户在庞大的数据量中有效过滤和捕捉数据并进行高质量的运用,个性化推荐算法被提出。该算法在不同场景应用中不断发展,对用户的感知与决策行为产生导向作用。集中研究了基于协同过滤的推荐、基于内容的推荐、基于关联规则的推荐和混合推荐4种典型的个性化推荐算法,分析其在大数据环境下和不同场景中的特点和适用性;从互联网内容平台、电子商务平台和社交场景视角,探究个性化推荐算法在相关理论引入和新兴技术融入中不断发展的进程;从使用意愿和购买决策两方面的影响展开探索,发现了个性化推荐算法对用户决策行为的影响机制,进而探讨了个性化推荐算法在用户决策中的功能作用,并对相关研究进行展望。

Abstract: The rapid development of the Internet has generated massive data,and the phenomenon of information overload has thus become increasingly prominent.In order to help users effectively filter and capture data and make high-quality use of the huge amount of data,personalized recommendation algorithms have been proposed,and have been continuously developed in different scenarios and applications,and have a guiding role in the perception and decision-making behaviors of users.This paper focuses on four typical personalized recommendation algorithms,namely,collaborative filtering-based recommendation,content-based recommendation,association rule-based recommendation,and hybrid recommendation,analyzes their characteristics and applicability in the big data environment and different scenarios,and explores the development of personalized recommendation algorithms in the introduction of relevant theories and integration of emerging technologies from the perspective of Internet content platforms,e-commerce platforms,and social scenarios.The study also explores the influence of personalized recommendation algorithms on users’ decision-making behavior from the perspective of willingness to use and purchase decision,and then explores the functional role of personalized recommendation algorithms in users’ decision-making and related research prospects.

Key words: Personalized recommendations, Algorithm optimization, User decision-making behavior, E-commerce, Collaborative filtering, Machine learning

中图分类号:

TP391

徐富萍, 周晓航, 张宁. 个性化推荐算法对用户决策行为影响研究综述[J]. 计算机科学, 2025, 52(11A): 241100086-8. https://doi.org/10.11896/jsjkx.241100086

XU Fuping, ZHOU Xiaohang, ZHANG Ning. Review of Impact of Personalized Recommendation Algorithms on User Decision-makingBehavior[J]. Computer Science, 2025, 52(11A): 241100086-8. https://doi.org/10.11896/jsjkx.241100086

参考文献

[1]LI M,WANG L.A survey on personalized news recommendation technology[J].IEEE Access,2019,7:145861-145879.
[2]LI J,YUE R,LIN W,et al.Study on the attractiveness of social e-commerce platforms from the consumers’ perspective based on the AISAS model[J].Journal of Computational Methods in Science and Engineering,2024,24(4／5):2517-2547.
[3]ZHANG S,YAO L,SUN A,et al.Deep learning based recommender system:A survey and new perspectives[J].ACM Computing Surveys(CSUR),2019,52(1):1-38.
[4]LIU R.Simulation of E-learning in English personalized learning recommendation system based on Markov chain algorithm and adaptive learning algorithm[J].Entertainment Computing,2024,51:100719.
[5]TROUSSAS C,KROUSKA A,KOLIARAKIS A,et al.Har-nessing the power of user-centric artificial intelligence:Customized recommendations and personalization in hybrid recommender systems[J].Computers,2023,12(5):109.
[6]GAO Y,HUANG Z W,HUANG Z Y,et al.Multi-scale broad collaborative filtering for personalized recommendation[J].Knowledge-based Systems,2023,278:110853.
[7]LUO L,XIE H,RAO Y,et al.Personalized recommendation by matrix co-factorization with tags and time information[J].Expert Systems with Applications,2019,119:311-321.
[8]FANG J,LI B,GAO M.Collaborative filtering recommendation algorithm based on deep neural network fusion[J].International Journal of Sensor Networks,2020,34(2):71-80.
[9]THAKKER U,PATEL R,SHAH M.A comprehensive analysison movie recommendation system employing collaborative filtering[J].Multimedia Tools and Applications,2021,80(19):28647-28672.
[10]CHEN Y,HUANG J.Effective Content Recommendation inNew Media:Leveraging Algorithmic Approaches[J].IEEE Access,2024.
[11]PéREZ-ALMAGUER Y,YERA R,ALZAHRANI A A,et al.Content-based group recommender systems:A general taxonomy and further improvements[J].Expert Systems with Applications,2021,184:115444.
[12]ALABDULJABBAR R,ALMAZROU H,ALDAWOD A.Context-Aware News Recommendation System:Incorporating Contextual Information and Collaborative Filtering Techniques[J].International Journal of Computational Intelligence Systems,2023,16(1):137.
[13]LUO Y.A personalised recommendation of mobile learningmodel based on content awareness[J].International Journal of Continuing Engineering Education and Life Long Learning,2023,33(2／3):299-312.
[14]D’ASARO F,DE LUCA S,BONGIOVANNI L,et al.Zero-Shot Content-Based Crossmodal Recommendation System[J].Expert Systems with Applications,2024,258:125108.
[15]LIAO M.Understanding the Effects of Personalized Recommender Systems on Political News Perceptions:A Comparison of Content-Based,Collaborative,and Editorial Choice-Based News Recommender System[J].Journal of Broadcasting & Electronic Media,2023,67(3):294-322.
[16]LEI H,SHAN X,JIANG L.Personalized Item Recommendation Algorithm for Outdoor Sports[J].Computational Intelligence and Neuroscience,2022,2022(1):8282257.
[17]ILARRI S,TRILLO-LADO R.An approach for proactive mobile recommendations based on user-defined rules[J].Expert Systems with Applications,2024,242:122714.
[18]SANTOSO M H.Application of association rule method using apriori algorithm to find sales patterns case study of indomaret tanjung anom[J].Brilliance:Research of Artificial Intelligence,2021,1(2):54-66.
[19]NAJAFABADI M K,MAHRIN M N,CHUPRAT S,et al.Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data[J].Computers in Human Behavior,2017,67:113-128.
[20]XIANG D,ZHANG Z.Cross-Border E-Commerce Personalized Recommendation Based on Fuzzy Association Specifications Combined with Complex Preference Model[J].Mathematical Problems in Engineering,2020,2020(1):8871126.
[21]WEIDIG J,KUEHNL C.Improving the effectiveness of personalized recommendations through attributional cues[J].Psycho-logy & Marketing,2023,40(12):2559-2575.
[22]CABRERA-SÁNCHEZ J P,RAMOS-DE-LUNA I,CARVA-JAL-TRUJILLO E,et al.Online recommendation systems:Factors influencing use in e-commerce[J].Sustainability,2020,12(21):8888.
[23]LI T,LIU F,CHEN X,et al.Web log mining techniques to optimize Apriori association rule algorithm in sports data information management[J].Scientific Reports,2024,14(1):24099-24099.
[24]CHEN H,YANG M,TANG X.Association rule mining of aircraft event causes based on the Apriori algorithm[J].Scientific Reports,2024,14(1):13440.
[25]AFOUDI Y,LAZAAR M,AL ACHHAB M.Hybrid recommendation system combined content-based filtering and collaborative prediction using artificial neural network[J].Simulation Modelling Practice and Theory,2021,113:102375.
[26]MA D,WANG Y,MA J,et al.SGNR:A social graph neural network based interactive recommendation scheme for e-commerce[J].Tsinghua Science and Technology,2023,28(4):786-798.
[27]LI Q,LI X,LEE B,et al.A hybrid CNN-based review helpfulness filtering model for improving e-commerce recommendation Service[J].Applied Sciences,2021,11(18):8613.
[28]LIU J,YI H,GAO Y,et al.Personalized Point-of-Interest Re-commendation Using Improved Graph Convolutional Network in Location-Based Social Network[J].Electronics,2023,12(16):3495.
[29]YIN Z,HAN K,WANG P,et al.H3GNN:Hybrid Hierarchical HyperGraph Neural Network for Personalized Session-based Recommendation[J].ACM Transactions on Information Systems,2023,42(3):1-30.
[30]BERKANI L,ZEGHOUD S,KERBOUA I L.Neural hybrid re-commendation based on GMF and hybrid MLP[M]//Artificial Intelligence and Machine Learning for EDGE Computing.Academic Press,2022:287-303.
[31]WANG R,WU Z,LOU J,et al.Attention-based dynamic usermodeling and deep collaborative filtering recommendation[J].Expert Systems with Applications,2022,188:116036.
[32]HUANG J,VONG C M,CHEN C L P,et al.Accurate and efficient large-scale multi-label learning with reduced feature broad learning system using label correlation[J].IEEE Transactions on Neural Networks and Learning Systems,2022,34(12):10240-10253.
[33]CAI L,WEN W,WU B,et al.A coarse-to-fine user preferences prediction method for point-of-interest recommendation[J].Neurocomputing,2021,422:1-11.
[34]ABU-SALIH B,ALSAWALQAH H,ELSHQEIRAT B,et al.Toward a knowledge-based personalised recommender system for mobile app development[J].arXiv:1909.03733,2019.
[35]YU J,YIN H,LI J,et al.Self-supervised multi-channel hyper-graph convolutional network for social recommendation[C]//Proceedings of the Web Conference 2021.2021:413-424.
[36]ORAMA J A,BORRAS J,MORENO A.Combining cluster-based profiling based on social media features and association rule mining for personalised recommendations of touristic activities[J].Applied Sciences,2021,11(14):6512.
[37]LI J,LI C,LIU J,et al.Personalized mobile video recommendation based on user preference modeling by deep features and social tags[J].Applied Sciences,2019,9(18):3858.
[38]REMOUNTAKIS M,KOTIS K,KOURTZIS B,et al.UsingChatGPT and persuasive technology for personalized recommendation messages in hotel upselling[J].Information,2023,14(9):504.
[39]LATRECH J,KODIA Z,BEN A N.CODFI-DL:a hybrid recommender system combining enhanced collaborative and demographic filtering based on deep learning[J].The Journal of Supercomputing,2024,80(1):1160-1182.
[40]KUMAR V,GUPTA A K,GARG R R,et al.The ultimate re-commendation system:proposed Pranik System[J].Multimedia Tools and Applications,2024,83(14):43177-43198.
[41]WU C,WU F,HUANG Y,et al.Personalized news recommendation:Methods and challenges[J].ACM Transactions on Information Systems,2023,41(1):1-50.
[42]EDIZEL B,BONCHI F,HAJIAN S,et al.FaiRecSys:mitigating algorithmic bias in recommender systems[J].International Journal of Data Science and Analytics,2020,9:197-213.
[43]WANG S F,GONG M G,WU Y,et alMulti-objective optimization for location-based and preferences-aware recommendation[J].Information Sciences,2020.
[44]KASHEF R,PUN H.Predicting l-CrossSold products usingconnected components:A clustering-based recommendation system[J].Electronic Commerce Research and Applications,2022,53:101148.
[45]NGUYEN T,HSU P F.More personalized,more useful?Reinvestigating recommendation mechanisms in e-commerce[J].International Journal of Electronic Commerce,2022,26(1):90-122.
[46]LIU S,SUN J,DENG X,et al.Towards platform profit-aware fairness in personalized recommendation[J].International Journal of Machine Learning and Cybernetics,2024:1-16.
[47]ZHANG E,MA W,ZHANG J,et al.A Service Recommendation System Based on Dynamic User Groups and Reinforcement Learning[J].Electronics,2023,12(24):5034.
[48]LIANG Q,LIAO X,SHANG J.A multiple criteria approach integrating social ties to support purchase decision[J].Computers &Industrial Engineering,2020,147:106655.
[49]YANG Z S,CHENG J Y.Recommendation algorithm based on knowledge graph to propagate user preference[J].International Journal of Computational Intelligence System,2021,14(1):1564-1576.
[50]SUHAIM A B,BERRI J.Directional user similarity model for personalized recommendation in online social networks[J].Journal of King Saud University-Computer and Information Sciences,2022,34(10):10205-10216.
[51]HUANG J,VONG C M,WANG G,et al.Joint label enhancement and label distribution learning via stacked graph regularization-based polynomial fuzzy broad learning system[J].IEEE Transactions on Fuzzy Systems,2023,31(9):3290-3304.
[52]NEVE J,PALOMARES I.Hybrid reciprocal recommender systems:Integrating item-to-user principles in reciprocal recommendation[C]//Companion Proceedings of the Web Conference 2020.2020:848-853.
[53]LIANG H Y.Intelligent Tourism Personalized Recommendation Based on Multi-Fusion of Clustering Algorithms[J].Advances in Multimedia,2021,2021(1):4517973.
[54]LEE M,OH S.An information recommendation technique based on influence and activeness of users in social networks[J].Applied Sciences,2021,11(6):2530.
[55]LAI C H,HSU C Y.Rating prediction based on combination of review mining and user preference analysis[J].Information Systems,2021,99:101742.
[56]TIAN J,SHI X,LI M.Price-aware matrix factorization modelfor personalized recommendations[J].Information & Management,2023,60(5):103815.
[57]PAUL A,WU Z,LIU K,et al.Personalized recommendation:From clothing to academic[J].Multimedia Tools and Applications,2022,81(10):14573-14588.
[58]MESSAOUDI F,LOUKILI M.E-commerce Personalized Re-commendations:a Deep Neural Collaborative Filtering Approach[C]//Operations Research Forum.Cham:Springer InternationalPublishing,2024.
[59]LYU Y,CHOW C Y,WANG R,et al.iMCRec:A multi-criteria framework for personalized point-of-interest recommendations[J].Information Sciences,2019,483:294-312.
[60]CHANG W,PARK J.A comparative study on the effect ofChatGPT recommendation and AI recommender systems on the formation of a consideration set[J].Journal of Retailing and Consumer Services,2024,78:103743.
[61]LI Y,WANG R,NAN G,et al.A personalized paper recommendation method considering diverse user preferences[J].Decision Support Systems,2021,146:113546.
[62]DAN Y,ZHENG T N,YANG F J.Time-Aware CF and Temporal Association Rule-Based Personalized Hybrid Recommender System [J].Journal of Organizational and End User Computing(JOEUC),2021,33(3):19-34.
[63]COUSSEMENT K,DE BOCK K W,GEUENS S.A decision-analytic framework for interpretable recommendation systems with multiple input data sources:a case study for a European etailer[J].Annals of Operations Research,2022,315(2):671-694.
[64]DONG M,YUAN F,YAO L,et al.A survey for trust-aware recommender systems:A deep learning perspective[J].Know-ledge-Based Systems,2022,249:108954.
[65]QIN H,XU W,ZHAO Y,et al.Personalized recommendations based on hotel online review data[J].Journal of Physics:Confe-rence Series.IOP Publishing,2020,1486(5):052028.
[66]CHENG F,YU S,QIN S,et al.User experience evaluationmethod based on online product reviews[J].Journal of Intelligent & Fuzzy Systems,2021,41(1):1791-1805.
[67]DING N,QIN Y,YANG G,et al.Parameter-efficient fine-tuning of large-scale pre-trained language models[J].Nature Machine Intelligence,2023,5(3):220-235.
[68]YANG X,LIU N.A Collaborative Filtering Algorithm Based on Sentiment Analysis in Review Texts[J].Automation and Machine Learning,2023,4(2):68-75.
[69]GAO L,LI J.E-Commerce Personalized Recommendation Model Based on Semantic Sentiment[J].Mobile Information Systems,2022,2022(1):7246802.
[70]LEX E,KOWALD D,SEITLINGER P,et al.Psychology-in-formed recommender systems[J].Foundations and trends in information retrieval,2021,15(2):134-242.
[71]SHANG Q,CHEN J,FU H,et al.“Guess You Like It”－How personalized recommendation timing and product type influence consumers’ acceptance:An ERP study[J].Neuroscience Letters,2023,807:137261.
[72]YI S,KIM D,JU J.Recommendation technologies and consumption diversity:An experimental study on product recommendations,consumer search,and sales diversity[J].Technological Forecasting and Social Change,2022,178:121486.
[73]AMOS C,ZHANG L.Consumer reactions to perceived undis-closed ChatGPT usage in an online review context[J].Telema-tics and Informatics,2024,93:102163.

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