Literatura académica sobre el tema "Trust-based collaborative filtering"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "Trust-based collaborative filtering".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "Trust-based collaborative filtering"
Zeng, Yejia y Zehui Qu. "Trust-Based Neural Collaborative Filtering". Journal of Physics: Conference Series 1229 (mayo de 2019): 012051. http://dx.doi.org/10.1088/1742-6596/1229/1/012051.
Texto completoNgwawe, Edwin, Elisha Abade y Stephen Mburu. "Trust Enhanced Collaborative Filtering Recommendation Algorithm". International Research Journal of Computer Science 10, n.º 04 (31 de mayo de 2023): 88–96. http://dx.doi.org/10.26562/irjcs.2023.v1004.10.
Texto completoKim, Hyoung Do. "Applying Consistency-Based Trust Definition to Collaborative Filtering". KSII Transactions on Internet and Information Systems 3, n.º 4 (30 de agosto de 2009): 366–75. http://dx.doi.org/10.3837/tiis.2009.04.002.
Texto completoDuan, Miao. "Collaborative Filtering Recommendation Algorithm based on Trust Propagation". International Journal of Security and Its Applications 9, n.º 7 (31 de julio de 2015): 99–108. http://dx.doi.org/10.14257/ijsia.2015.9.7.09.
Texto completoGuo, Liangmin, Jiakun Liang, Ying Zhu, Yonglong Luo, Liping Sun y Xiaoyao Zheng. "Collaborative filtering recommendation based on trust and emotion". Journal of Intelligent Information Systems 53, n.º 1 (14 de julio de 2018): 113–35. http://dx.doi.org/10.1007/s10844-018-0517-4.
Texto completoFaridani, Vahid, Mehrdad Jalali y Majid Vafaei Jahan. "Collaborative filtering-based recommender systems by effective trust". International Journal of Data Science and Analytics 3, n.º 4 (15 de marzo de 2017): 297–307. http://dx.doi.org/10.1007/s41060-017-0049-y.
Texto completoYuan, Zahir y Yang. "Modeling Implicit Trust in Matrix Factorization-Based Collaborative Filtering". Applied Sciences 9, n.º 20 (16 de octubre de 2019): 4378. http://dx.doi.org/10.3390/app9204378.
Texto completoLiu, Duen-Ren, Chin-Hui Lai y Hsuan Chiu. "Sequence-based trust in collaborative filtering for document recommendation". International Journal of Human-Computer Studies 69, n.º 9 (agosto de 2011): 587–601. http://dx.doi.org/10.1016/j.ijhcs.2011.06.001.
Texto completoYeh, Tzu-Yu y Rasha Kashef. "Trust-Based Collaborative Filtering Recommendation Systems on the Blockchain". Advances in Internet of Things 10, n.º 04 (2020): 37–56. http://dx.doi.org/10.4236/ait.2020.104004.
Texto completoChen, Hailong, Haijiao Sun, Miao Cheng y Wuyue Yan. "A Recommendation Approach for Rating Prediction Based on User Interest and Trust Value". Computational Intelligence and Neuroscience 2021 (6 de marzo de 2021): 1–9. http://dx.doi.org/10.1155/2021/6677920.
Texto completoTesis sobre el tema "Trust-based collaborative filtering"
Ercan, Eda. "Probabilistic Matrix Factorization Based Collaborative Filtering With Implicit Trust Derived From Review Ratings Information". Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612529/index.pdf.
Texto completoSahinkaya, Ferhat. "A Content Boosted Collaborative Filtering Approach For Recommender Systems Based On Multi Level And Bidirectional Trust Data". Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612013/index.pdf.
Texto completopreferable data&rdquo
that has not already been consumed by the user. Traditional approaches use user/item similarity or item content information to filter items for the active user
however most of the recent approaches also consider the trustworthiness of users. By using trustworthiness, only reliable users according to the target user opinion will be considered during information retrieval. Within this thesis work, a content boosted method of using trust data in recommender systems is proposed. It is aimed to be shown that people who trust the active user and the people, whom the active user trusts, also have correlated opinions with the active user. This results the fact that the rated items by these people can also be used while offering new items to users. For this research, www.epinions.com site is crawled, in order to access user trust relationships, product content information and review ratings which are ratings given by users to product reviews that are written by other users.
Nzekon, Nzeko'o Armel Jacques. "Système de recommandation avec dynamique temporelle basée sur les flots de liens". Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS454.
Texto completoRecommending appropriate items to users is crucial in many e-commerce platforms that propose a large number of items to users. Recommender systems are one favorite solution for this task. Most research in this area is based on explicit ratings that users give to items, while most of the time, ratings are not available in sufficient quantities. In these situations, it is important that recommender systems use implicit data which are link stream connecting users to items while maintaining timestamps i.e. users browsing, purchases and streaming history. We exploit this type of implicit data in this thesis. One common approach consists in selecting the N most relevant items to each user, for a given N, which is called top-N recommendation. To do so, recommender systems rely on various kinds of information, like content-based features of items, past interest of users for items and trust between users. However, they often use only one or two such pieces of information simultaneously, which can limit their performance because user's interest for an item can depend on more than two types of side information. To address this limitation, we make three contributions in the field of graph-based recommender systems. The first one is an extension of the Session-based Temporal Graph (STG) introduced by Xiang et al., which is a dynamic graph combining long-term and short-term preferences in order to better capture user preferences over time. STG ignores content-based features of items, and make no difference between the weight of newer edges and older edges. The new proposed graph Time-weight Content-based STG addresses STG limitations by adding a new node type for content-based features of items, and a penalization of older edges. The second contribution is the Link Stream Graph (LSG) for temporal recommendations. This graph is inspired by a formal representation of link stream, and has the particularity to consider time in a continuous way unlike others state-of-the-art graphs, which ignore the temporal dimension like the classical bipartite graph (BIP), or consider time discontinuously like STG where time is divided into slices. The third contribution in this thesis is GraFC2T2, a general graph-based framework for top-N recommendation. This framework integrates basic recommender graphs, and enriches them with content-based features of items, users' preferences temporal dynamics, and trust relationships between them. Implementations of these three contributions on CiteUlike, Delicious, Last.fm, Ponpare, Epinions and Ciao datasets confirm their relevance
AZUIRSON, Gabriel de Albuquerque Veloso. "Investigação da combinação de filtragem colaborativa e recomendação baseada em confiança através de medidas de esparsidade". Universidade Federal de Pernambuco, 2015. https://repositorio.ufpe.br/handle/123456789/15900.
Texto completoMade available in DSpace on 2016-03-11T15:25:20Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) dissertação_gava_cin.pdf: 1596983 bytes, checksum: 23245c1b65fe3416d3baeeac5e118845 (MD5) Previous issue date: 2015-08-06
Sistemas de recomendação têm desempenhado um papel importante em diferentes contextos de aplicação (e.g recomendação de produtos, filmes, músicas, livros, dentre outros). Eles automaticamente sugerem a cada usuário itens que podem ser relevantes, evitando que o usuário tenha que analisar uma quantidade gigantesca de itens para realizar sua escolha. Filtragem colaborativa (FC) é a abordagem mais popular para a construção de sistemas de recomendação, embora sofra com problemas relacionados à esparsidade dos dados (e.g., usuários ou itens com poucas avaliações). Neste trabalho, investigamos a combinação de técnicas de FC, representada pela técnica de Fatoração de Matrizes, e técnicas de recomendação baseada em confiança (RBC) em redes sociais para aliviar o problema da esparsidade dos dados. Sistemas de RBC têm se mostrado de fato efetivos para aumentar a qualidade das recomendações, em especial para usuários com poucas avaliações realizadas (e.g., usuários novos). Entretanto, o desempenho relativo entre técnicas de FC e de RBC pode depender da quantidade de informação útil presente nas bases de dados. Na arquitetura proposta nesse trabalho, as predições geradas por técnicas de FC e de RBC são combinadas de forma ponderada através de medidas de esparsidade calculadas para usuários e itens. Para isso, definimos inicialmente um conjunto de medidas de esparsidade que serão calculadas sobre a matriz de avaliações usuários-itens e matriz de confiança usuários-usuários. Através de experimentos realizados utilizando a base de dados Epinions, observamos que a proposta de combinação trouxe uma melhoria nas taxas de erro e na cobertura em comparação com as técnicas isoladamente.
Recommender systems have played an important role in different application contexts (e.g recommendation of products, movies, music, books, among others). They automatically suggest each user items that may be relevant, preventing the user having to analyze a huge amount of items to make your choice. Collaborative filtering (CF) is the most popular approach for building recommendation systems, although suffering with sparsity of the data-related issues (eg, users or items with few evaluations). In this study, we investigated the combination of CF techniques represented by matrix factorization technique, and trust-based recommendation techniques (TBR) on social networks to alleviate the problem of data sparseness. TBR systems have in fact proven to be effective to increase the quality of the recommendations, especially for users with few assessments already carried out (e.g., cold start users). However, the relative performance between CF and TBR techniques may depend on the amount of useful information contained in the databases. In the proposed architecture in this work, the predictions generated by CF and TBR techniques are weighted combined through sparsity measures calculated to users and items. To do this, first we define a set of sparsity measures that will be calculated on the matrix of ratings users-items and matrix of trust users-users. Through experiments using Epinions database, we note that the proposed combination brought an improvement in error rates and coverage compared to combined techniques.
Alghamedy, Fatemah. "ENHANCE NMF-BASED RECOMMENDATION SYSTEMS WITH AUXILIARY INFORMATION IMPUTATION". UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/79.
Texto completoLu, Chia-Ju y 呂佳如. "Item-level Trust-based Collaborative Filtering Approach to Recommender Systems". Thesis, 2008. http://ndltd.ncl.edu.tw/handle/v57w34.
Texto completo國立中山大學
資訊管理學系研究所
96
With the rapid growth of Internet, more and more information is disseminated in the World Wide Web. It is therefore not an easy task to acquire desired information from the Web environment due to the information overload problem. To overcome this difficulty, two major methods, information retrieval and information filtering, arise. Recommender systems that employ information filtering techniques also emerge when the users’ requirements are too vague in mind to express explicitly as keywords. Collaborative filtering (CF) refers to compare novel information with common interests shared by a group of people for recommendation purpose. But CF has major problem: sparsity. This problem refers to the situation that the coverage of ratings appears very sparse. With few data available, the user similarity employed in CF becomes unstable and thus unreliable in the recommendation process. Recently, several collaborative filtering variations arise to tackle the sparsity problem. One of them refers to the item-based CF as opposed to the traditional user-based CF. This approach focuses on the correlations of items based on users’ co-rating. Another popular variation is the trust-based CF. In such an approach, a second component, trust, is taken into account and employed in the recommendation process. The objective of this research is thus to propose a hybrid approach that takes both advantages into account for better performance. We propose the item-level trust-based collaborative filtering (ITBCF) approach to alleviate the sparsity problem. We observe that ITBCF outperforms TBCF in every situation we consider. It therefore confirms our conjecture that the item-level trusts that consider neighbors can stabilize derived trust values, and thus improve the performance.
Chou, Yun-Cheng y 周運城. "A Study of Collaborative Filtering Recommendation Approaches based on Trust Network". Thesis, 2012. http://ndltd.ncl.edu.tw/handle/74956432119771320206.
Texto completo國立交通大學
管理學院資訊管理學程
100
Recommender systems are successfully applied to many fields. Items that users may be interested in are recommended automatically. Therefore, users can quickly obtain personalized information from huge data and avoid information overloading. Among recommendation approaches, collaborative filtering (CF) predicts user interests of items merely based on user opinions, and is quite suit for social network and e-commerce services. Recently, some studies utilized trust network that are composed of friend relations to improve the accuracy and coverage of conventional CF. Additional benefit is preventing attack of malicious users, thus preserves the reliability of recommender system. However, there are no complete evaluation on the recommendation effectiveness of combination of explicit friend relation trust and implicit rating-based trust. Moreover, previous studies do not consider the variation of prediction accuracy with respect to different types of user such as cold-start user and heavy raters. We propose a novel trust-network CF recommendation approach, where trust network is constructed by the hybrid of explicit friend trust links and implicit rating-based trust links. Then, the trust network is extended by using rating-based trust links to discover more effective neighbors. In addition, we apply a dynamic method to adjust relative importance of explicit and implicit trust links. Experiments on Epinions dataset show that our approach outperforms conventional approaches in terms of the number of effective neighbors found and recommendation precision and recall. The results also show that conventional approaches have good performance for cold-start users, but fail to handle the existence of heavy raters properly.
Tsai, Chin-Chi y 蔡金琪. "Combining Multi-Criteria Rating-Based Trust and Collaborative Filtering for Document Recommendation". Thesis, 2013. http://ndltd.ncl.edu.tw/handle/90685221966803760200.
Texto completo國立交通大學
管理學院資訊管理學程
101
Collaborative filtering recommender systems have proven to solving information overload problems, and are widely implemented in various industry domains, such as books, music, video rentals, hotels, and so on. Nowadays, many recommender systems are extended from single-criterion rating to multi-criteria rating systems in order to improve the quality of the recommendations by selecting more similar neighbors based on multi-criteria ratings. The researches related to multi-criteria rating recommender systems mainly focus on predicting overall score and pursuing maximal utility for producing recommendation lists. However, some multi-criteria rating recommender systems do not have overall rating. Accordingly, producing a proper recommendation list in such systems become challenging due to a lack of final judging (overall rating) of their users. In some circumstances, such as conflicts among criteria, pursuing maximal utility could not be treated as best strategies because some criteria could not be replaced with others. In this paper, we incorporate multi-criteria ratings into the conventional trust-based techniques and propose a hybrid model combining multi-criteria rating-based trust and collaborative filtering techniques. To eliminate the problems mentioned above, we propose a 2-step recommendation process: (1) setting a set of recommendation conditions as recommendation filters, and (2) applying three recommendation policies to recommend items. The experiments show that our proposed approaches can increase the recommendation quality of multi-criteria rating recommender systems without overall rating.
Peng, Ting-Chun y 彭鼎鈞. "Trust-enhanced Blog Recommender System: iTrustUAn Integrated Approach Based on Multi-faceted Trust and Collaborative Filtering". Thesis, 2008. http://ndltd.ncl.edu.tw/handle/70869083747462544321.
Texto completo國立臺灣大學
資訊管理學研究所
96
The evolution of the Internet has given people access to information in a way never previously imagined; yet, ironically, it has given rise to the problem of information overload. Fortunately, the advent of recommender systems has relieved people of much of the effort required to find desired information. Blogs represent a new killer application on the Internet that gives users a channel to express themselves and share their knowledge and feelings with other people worldwide. The number of new blogs is growing exponentially. However, due to the diverse subjects covered by bloggers, it is difficult for readers to find blogs containing articles that fit their interests or information needs from the hundreds of thousands, possibly millions, of blogs on the Internet. Currently, most blog recommendation websites only provide search functions based on different types of blogs. In other words, they do not provide any customized or personalized blog article recommendations. Given the need to ease information overload in the blog domain, we have modified some existing approaches, and herein propose a novel trust-enhanced collaborative filtering approach that integrates multi-faceted trust based on article types and user similarity. We also designed an online blog article recommender system, called iTrustU to evaluate whether our proposed approach can improve the accuracy and quality of recommendations. During a 45-day online experiment with 179 participants from the Internet, we found that our system achieved good outcomes in both recommendation accuracy and user satisfaction. In contrast to traditional collaborative filtering approaches, which only consider user similarity or trust information, our integrated approach yields a significantly higher accuracy, especially for cold start users. Through statistical analysis, we prove that in the blogosphere community, trust and similarity among bloggers/readers exhibit a significantly positive correlation. This result is the same as that of past research. Our research results show that, through the exploitation and inference of trust relationships in a trust network, we can provide more effective recommender systems in terms of user satisfaction. The proposed approach not only applies to the blogosphere, but also to any online social community or commercial shopping/auction websites when trust relationships already exist between users on the fly.
Peng, Ting-Chun. "Trust-enhanced Blog Recommender System: iTrustU An Integrated Approach Based on Multi-faceted Trust and Collaborative Filtering". 2008. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-2307200800311500.
Texto completoCapítulos de libros sobre el tema "Trust-based collaborative filtering"
Luo, Tiejian, Su Chen, Guandong Xu y Jia Zhou. "Collaborative Filtering". En Trust-based Collective View Prediction, 25–51. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7202-5_3.
Texto completoWu, Qinzhu, Anders Forsman, Zukun Yu y William Wei Song. "A Computational Model for Trust-Based Collaborative Filtering". En Web Information Systems Engineering – WISE 2013 Workshops, 266–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54370-8_22.
Texto completoZhang, Fuzhi, Long Bai y Feng Gao. "A User Trust-Based Collaborative Filtering Recommendation Algorithm". En Information and Communications Security, 411–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-11145-7_32.
Texto completoMeyffret, Simon, Lionel Médini y Frédérique Laforest. "Confidence on Collaborative Filtering and Trust-Based Recommendations". En Lecture Notes in Business Information Processing, 162–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39878-0_15.
Texto completoKant, Vibhor y Kamal K. Bharadwaj. "Incorporating Fuzzy Trust in Collaborative Filtering Based Recommender Systems". En Swarm, Evolutionary, and Memetic Computing, 433–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27172-4_53.
Texto completoAbinaya, S. y M. K. Kavitha Devi. "Trust-Based Context-Aware Collaborative Filtering Using Denoising Autoencoder". En Pervasive Computing and Social Networking, 35–49. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5640-8_4.
Texto completoDuricic, Tomislav, Hussain Hussain, Emanuel Lacic, Dominik Kowald, Denis Helic y Elisabeth Lex. "Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering". En Lecture Notes in Computer Science, 181–91. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59491-6_17.
Texto completoQin, Xiaofan, Wenan Tan y Anqiong Tang. "A New Trust-Based Collaborative Filtering Measure Using Bhattacharyya Coefficient". En Computer Supported Cooperative Work and Social Computing, 399–407. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1377-0_31.
Texto completoDwivedi, Pragya y Kamal K. Bharadwaj. "Effective Resource Recommendations for E-learning: A Collaborative Filtering Framework Based on Experience and Trust". En Communications in Computer and Information Science, 166–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25734-6_26.
Texto completoLiao, Mingding, Xiao Liu, Xiaofeng Gao, Jiaofei Zhong y Guihai Chen. "iSim: An Efficient Integrated Similarity Based Collaborative Filtering Approach for Trust Prediction in Service-Oriented Social Networks". En Service-Oriented Computing, 501–16. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46295-0_31.
Texto completoActas de conferencias sobre el tema "Trust-based collaborative filtering"
Weng, Jianshu, Chunyan Miao, Angela Goh y Dongtao Li. "Trust-based collaborative filtering". En the 14th ACM international conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1099554.1099636.
Texto completoDuricic, Tomislav, Emanuel Lacic, Dominik Kowald y Elisabeth Lex. "Trust-based collaborative filtering". En RecSys '18: Twelfth ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3240323.3240404.
Texto completoWang, Jing, Jian Yin, Yuzhang Liu y Chuangguang Huang. "Trust-based Collaborative Filtering". En 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011). IEEE, 2011. http://dx.doi.org/10.1109/fskd.2011.6020048.
Texto completoXu, Xiaowei y Fudong Wang. "Trust -Based Collaborative Filtering Algorithm". En 2012 5th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2012. http://dx.doi.org/10.1109/iscid.2012.88.
Texto completoMauro, Noemi, Liliana Ardissono y Zhongli Filippo Hu. "Multi-faceted Trust-based Collaborative Filtering". En UMAP '19: 27th Conference on User Modeling, Adaptation and Personalization. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3320435.3320441.
Texto completoWeng, Jianshu, Chunyan Miao y Angela Goh. "Improving collaborative filtering with trust-based metrics". En the 2006 ACM symposium. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1141277.1141717.
Texto completoYuhan, Mao. "A Novel Collaborative Filtering Algorithm Based on Trust". En 2018 Eighth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC). IEEE, 2018. http://dx.doi.org/10.1109/imccc.2018.00172.
Texto completoJing-xia, Ren y Wu Zhi-feng. "Collaborative Filtering Algorithm Based on Dynamic Trust Attenuation". En ICBDT 2020: 2020 3rd International Conference on Big Data Technologies. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3422713.3422727.
Texto completoChen, Xinxin, Yu Guo, Yang Yang y Zhenqiang Mi. "Trust-based collaborative filtering algorithm in social network". En 2016 International Conference on Computer, Information and Telecommunication Systems (CITS). IEEE, 2016. http://dx.doi.org/10.1109/cits.2016.7546412.
Texto completoMoghaddam, Morteza Ghorbani, Norwati Mustapha, Aida Mustapha, Nurfadhlina Mohd Sharef y Anousheh Elahian. "AgeTrust: A New Temporal Trust-Based Collaborative Filtering Approach". En 2014 International Conference on Information Science and Applications (ICISA). IEEE, 2014. http://dx.doi.org/10.1109/icisa.2014.6847352.
Texto completo