Log in

Relevant bibliographies by topics / Trust-based collaborative filtering

Contents

Journal articles
Dissertations / Theses
Book chapters
Conference papers

Academic literature on the topic 'Trust-based collaborative filtering'

Author: Grafiati

Published: 25 May 2024

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Trust-based collaborative filtering.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Trust-based collaborative filtering"

1

Zeng, Yejia, and Zehui Qu. "Trust-Based Neural Collaborative Filtering." Journal of Physics: Conference Series 1229 (May 2019): 012051. http://dx.doi.org/10.1088/1742-6596/1229/1/012051.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Ngwawe, Edwin, Elisha Abade, and Stephen Mburu. "Trust Enhanced Collaborative Filtering Recommendation Algorithm." International Research Journal of Computer Science 10, no. 04 (2023): 88–96. http://dx.doi.org/10.26562/irjcs.2023.v1004.10.

Full text

Abstract:

Recommender systems have been designed to suggest to users the suitable items based on the user profiles and therefore reduce the danger of information overload, however, the recommender systems are still prone to profile injection attacks which then exposes a user to a potential fraud, which leads to a sense of untrustworthiness and reduced accuracy due to malicious manipulations. In this research, we developed a model which should be embedded into the recommendation pipeline in order to improve trustworthiness of recommender system output. We extended the classical collaborative recommendation algorithm by adding a new trust parameter and then compare the prediction accuracy of the trust enhanced collaborative filtering algorithm against that of the classical collaborative filtering algorithm using Mean Absolute Error and Root Mean Square Error and then test the hypothesis using t-test. We found that the new trust parameter improves the accuracy of collaborating filtering algorithm significantly.

APA, Harvard, Vancouver, ISO, and other styles

3

Kim, Hyoung Do. "Applying Consistency-Based Trust Definition to Collaborative Filtering." KSII Transactions on Internet and Information Systems 3, no. 4 (2009): 366–75. http://dx.doi.org/10.3837/tiis.2009.04.002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Duan, Miao. "Collaborative Filtering Recommendation Algorithm based on Trust Propagation." International Journal of Security and Its Applications 9, no. 7 (2015): 99–108. http://dx.doi.org/10.14257/ijsia.2015.9.7.09.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Guo, Liangmin, Jiakun Liang, Ying Zhu, Yonglong Luo, Liping Sun, and Xiaoyao Zheng. "Collaborative filtering recommendation based on trust and emotion." Journal of Intelligent Information Systems 53, no. 1 (2018): 113–35. http://dx.doi.org/10.1007/s10844-018-0517-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Faridani, Vahid, Mehrdad Jalali, and Majid Vafaei Jahan. "Collaborative filtering-based recommender systems by effective trust." International Journal of Data Science and Analytics 3, no. 4 (2017): 297–307. http://dx.doi.org/10.1007/s41060-017-0049-y.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Yuan, Zahir, and Yang. "Modeling Implicit Trust in Matrix Factorization-Based Collaborative Filtering." Applied Sciences 9, no. 20 (2019): 4378. http://dx.doi.org/10.3390/app9204378.

Full text

Abstract:

Recommendation systems often use side information to both alleviate problems, such as the cold start problem and data sparsity, and increase prediction accuracy. One such piece of side information, which has been widely investigated in addressing such challenges, is trust. However, the difficulty in obtaining explicit relationship data has led researchers to infer trust values from other means such as the user-to-item relationship. This paper proposes a model to improve prediction accuracy by applying the trust relationship between the user and item ratings. Two approaches to implement trust into prediction are proposed: one involves the use of estimated trust, and the other involves the initial trust. The efficiency of the proposed method is verified by comparing the obtained results with four well-known methods, including the state-of-the-art deep learning-based method of neural graph collaborative filtering (NGCF). The experimental results demonstrate that the proposed method performs significantly better than the NGCF, and the three other matrix factorization methods, namely, the singular value decomposition (SVD), SVD++, and the social matrix factorization (SocialMF).

APA, Harvard, Vancouver, ISO, and other styles

8

Liu, Duen-Ren, Chin-Hui Lai, and Hsuan Chiu. "Sequence-based trust in collaborative filtering for document recommendation." International Journal of Human-Computer Studies 69, no. 9 (2011): 587–601. http://dx.doi.org/10.1016/j.ijhcs.2011.06.001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Yeh, Tzu-Yu, and Rasha Kashef. "Trust-Based Collaborative Filtering Recommendation Systems on the Blockchain." Advances in Internet of Things 10, no. 04 (2020): 37–56. http://dx.doi.org/10.4236/ait.2020.104004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Chen, Hailong, Haijiao Sun, Miao Cheng, and Wuyue Yan. "A Recommendation Approach for Rating Prediction Based on User Interest and Trust Value." Computational Intelligence and Neuroscience 2021 (March 6, 2021): 1–9. http://dx.doi.org/10.1155/2021/6677920.

Full text

Abstract:

Collaborative filtering recommendation algorithm is one of the most researched and widely used recommendation algorithms in personalized recommendation systems. Aiming at the problem of data sparsity existing in the traditional collaborative filtering recommendation algorithm, which leads to inaccurate recommendation accuracy and low recommendation efficiency, an improved collaborative filtering algorithm is proposed in this paper. The algorithm is improved in the following three aspects: firstly, considering that the traditional scoring similarity calculation excessively relies on the common scoring items, the Bhattacharyya similarity calculation is introduced into the traditional calculation formula; secondly, the trust weight is added to accurately calculate the direct trust value and the trust transfer mechanism is introduced to calculate the indirect trust value between users; finally, the user similarity and user trust are integrated, and the prediction result is generated by the trust weighting method. Experiments show that the proposed algorithm can effectively improve the prediction accuracy of recommendations.

APA, Harvard, Vancouver, ISO, and other styles

Dissertations / Theses on the topic "Trust-based collaborative filtering"

1

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Sahinkaya, 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.

Full text

Abstract:

As the Internet became widespread all over the world, people started to share great amount of data on the web and almost every people joined different data networks in order to have a quick access to data shared among people and survive against the information overload on the web. Recommender systems are created to provide users more personalized information services and to make data available for people without an extra effort. Most of these systems aim to get or learn user preferences, explicitly or implicitly depending to the system, and guess &ldquo<br>preferable data&rdquo<br>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<br>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.

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

Abstract:

La recommandation des produits appropriés aux clients est cruciale dans de nombreuses plateformes de e-commerce qui proposent un grand nombre de produits. Les systèmes de recommandation sont une solution favorite pour la réalisation de cette tâche. La majorité des recherches de ce domaine reposent sur des notes explicites que les utilisateurs attribuent aux produits, alors que la plupart du temps ces notes ne sont pas disponibles en quantité suffisante. Il est donc important que les systèmes de recommandation utilisent les données implicites que sont des flots de liens représentant les relations entre les utilisateurs et les produits, c'est-à-dire l'historique de navigation, des achats et de streaming. C'est ce type de données implicites que nous exploitons. Une approche populaire des systèmes de recommandation consiste, pour un entier N donné, à proposer les N produits les plus pertinents pour chaque utilisateur : on parle de recommandation top-N. Pour ce faire, bon nombre de travaux reposent sur des informations telles que les caractéristiques des produits, les goûts et préférences antérieurs des utilisateurs et les relations de confiance entre ces derniers. Cependant, ces systèmes n'utilisent qu'un ou deux types d'information simultanément, ce qui peut limiter leurs performances car l'intérêt qu'un utilisateur a pour un produit peut à la fois dépendre de plus de deux types d'information. Pour remédier à cette limite, nous faisons trois propositions dans le cadre des graphes de recommandation. La première est une extension du Session-based Temporal Graph (STG) introduit par Xiang et al., et qui est un graphe dynamique combinant les préférences à long et à court terme des utilisateurs, ce qui permet de mieux capturer la dynamique des préférences de ces derniers. STG ne tient pas compte des caractéristiques des produits et ne fait aucune différence de poids entre les arêtes les plus récentes et les arêtes les plus anciennes. Le nouveau graphe proposé, Time-weight content-based STG contourne les limites du STG en y intégrant un nouveau type de nœud pour les caractéristiques des produits et une pénalisation des arêtes les plus anciennes. La seconde contribution est un système de recommandation basé sur l'utilisation de Link Stream Graph (LSG). Ce graphe est inspiré d'une représentation des flots de liens et a la particularité de considérer le temps de manière continue contrairement aux autres graphes de la littérature, qui soit ignore la dimension temporelle comme le graphe biparti classique (BIP), soit considère le temps de manière discontinue avec un découpage du temps en tranches comme STG<br>Recommending 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

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

Abstract:

Submitted by Haroudo Xavier Filho (haroudo.xavierfo@ufpe.br) on 2016-03-11T15:25:20Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) dissertação_gava_cin.pdf: 1596983 bytes, checksum: 23245c1b65fe3416d3baeeac5e118845 (MD5)<br>Made 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<br>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.<br>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.

APA, Harvard, Vancouver, ISO, and other styles

5

Alghamedy, Fatemah. "ENHANCE NMF-BASED RECOMMENDATION SYSTEMS WITH AUXILIARY INFORMATION IMPUTATION." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/79.

Full text

Abstract:

This dissertation studies the factors that negatively impact the accuracy of the collaborative filtering recommendation systems based on nonnegative matrix factorization (NMF). The keystone in the recommendation system is the rating that expresses the user's opinion about an item. One of the most significant issues in the recommendation systems is the lack of ratings. This issue is called "cold-start" issue, which appears clearly with New-Users who did not rate any item and New-Items, which did not receive any rating. The traditional recommendation systems assume that users are independent and identically distributed and ignore the connections among users whereas the recommendation actually is a social activity. This dissertation aims to enhance NMF-based recommendation systems by utilizing the imputation method and limiting the errors that are introduced in the system. External information such as trust network and item categories are incorporated into NMF-based recommendation systems through the imputation. The proposed approaches impute various subsets of the missing ratings. The subsets are defined based on the total number of the ratings of the user or item before the imputation, such as impute the missing ratings of New-Users, New-Items, or cold-start users or items that suffer from the lack of the ratings. In addition, several factors are analyzed that affect the prediction accuracy when the imputation method is utilized with NMF-based recommendation systems. These factors include the total number of the ratings of the user or item before the imputation, the total number of imputed ratings for each user and item, the average of imputed rating values, and the value of imputed rating values. In addition, several strategies are applied to select the subset of missing ratings for the imputation that lead to increasing the prediction accuracy and limiting the imputation error. Moreover, a comparison is conducted with some popular methods that are in common with the proposed method in utilizing the imputation to handle the lack of ratings, but they differ in the source of the imputed ratings. Experiments on different large-size datasets are conducted to examine the proposed approaches and analyze the effects of the imputation on accuracy. Users and items are divided into three groups based on the total number of the ratings before the imputation is applied and their recommendation accuracy is calculated. The results show that the imputation enhances the recommendation system by capacitating the system to recommend items to New-Users, introduce New-Items to users, and increase the accuracy of the cold-start users and items. However, the analyzed factors play important roles in the recommendation accuracy and limit the error that is introduced from the imputation.

APA, Harvard, Vancouver, ISO, and other styles

6

Lu, Chia-Ju, and 呂佳如. "Item-level Trust-based Collaborative Filtering Approach to Recommender Systems." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/v57w34.

Full text

Abstract:

碩士<br>國立中山大學<br>資訊管理學系研究所<br>96<br>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.

APA, Harvard, Vancouver, ISO, and other styles

7

Chou, Yun-Cheng, and 周運城. "A Study of Collaborative Filtering Recommendation Approaches based on Trust Network." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/74956432119771320206.

Full text

Abstract:

碩士<br>國立交通大學<br>管理學院資訊管理學程<br>100<br>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.

APA, Harvard, Vancouver, ISO, and other styles

8

Tsai, Chin-Chi, and 蔡金琪. "Combining Multi-Criteria Rating-Based Trust and Collaborative Filtering for Document Recommendation." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/90685221966803760200.

Full text

Abstract:

碩士<br>國立交通大學<br>管理學院資訊管理學程<br>101<br>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.

APA, Harvard, Vancouver, ISO, and other styles

9

Peng, Ting-Chun, and 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.

Full text

Abstract:

碩士<br>國立臺灣大學<br>資訊管理學研究所<br>96<br>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.

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Trust-based collaborative filtering"

1

Luo, Tiejian, Su Chen, Guandong Xu, and Jia Zhou. "Collaborative Filtering." In Trust-based Collective View Prediction. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7202-5_3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Wu, Qinzhu, Anders Forsman, Zukun Yu, and William Wei Song. "A Computational Model for Trust-Based Collaborative Filtering." In Web Information Systems Engineering – WISE 2013 Workshops. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54370-8_22.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Zhang, Fuzhi, Long Bai, and Feng Gao. "A User Trust-Based Collaborative Filtering Recommendation Algorithm." In Information and Communications Security. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-11145-7_32.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Meyffret, Simon, Lionel Médini, and Frédérique Laforest. "Confidence on Collaborative Filtering and Trust-Based Recommendations." In Lecture Notes in Business Information Processing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39878-0_15.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Kant, Vibhor, and Kamal K. Bharadwaj. "Incorporating Fuzzy Trust in Collaborative Filtering Based Recommender Systems." In Swarm, Evolutionary, and Memetic Computing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27172-4_53.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Abinaya, S., and M. K. Kavitha Devi. "Trust-Based Context-Aware Collaborative Filtering Using Denoising Autoencoder." In Pervasive Computing and Social Networking. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5640-8_4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Duricic, Tomislav, Hussain Hussain, Emanuel Lacic, Dominik Kowald, Denis Helic, and Elisabeth Lex. "Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59491-6_17.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Qin, Xiaofan, Wenan Tan, and Anqiong Tang. "A New Trust-Based Collaborative Filtering Measure Using Bhattacharyya Coefficient." In Computer Supported Cooperative Work and Social Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1377-0_31.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Dwivedi, Pragya, and Kamal K. Bharadwaj. "Effective Resource Recommendations for E-learning: A Collaborative Filtering Framework Based on Experience and Trust." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25734-6_26.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Liao, Mingding, Xiao Liu, Xiaofeng Gao, Jiaofei Zhong, and Guihai Chen. "iSim: An Efficient Integrated Similarity Based Collaborative Filtering Approach for Trust Prediction in Service-Oriented Social Networks." In Service-Oriented Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46295-0_31.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Trust-based collaborative filtering"

1

Weng, Jianshu, Chunyan Miao, Angela Goh, and Dongtao Li. "Trust-based collaborative filtering." In the 14th ACM international conference. ACM Press, 2005. http://dx.doi.org/10.1145/1099554.1099636.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Duricic, Tomislav, Emanuel Lacic, Dominik Kowald, and Elisabeth Lex. "Trust-based collaborative filtering." In RecSys '18: Twelfth ACM Conference on Recommender Systems. ACM, 2018. http://dx.doi.org/10.1145/3240323.3240404.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Wang, Jing, Jian Yin, Yuzhang Liu, and Chuangguang Huang. "Trust-based Collaborative Filtering." In 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011). IEEE, 2011. http://dx.doi.org/10.1109/fskd.2011.6020048.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Xu, Xiaowei, and Fudong Wang. "Trust -Based Collaborative Filtering Algorithm." In 2012 5th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2012. http://dx.doi.org/10.1109/iscid.2012.88.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Mauro, Noemi, Liliana Ardissono, and Zhongli Filippo Hu. "Multi-faceted Trust-based Collaborative Filtering." In UMAP '19: 27th Conference on User Modeling, Adaptation and Personalization. ACM, 2019. http://dx.doi.org/10.1145/3320435.3320441.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Weng, Jianshu, Chunyan Miao, and Angela Goh. "Improving collaborative filtering with trust-based metrics." In the 2006 ACM symposium. ACM Press, 2006. http://dx.doi.org/10.1145/1141277.1141717.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Yuhan, Mao. "A Novel Collaborative Filtering Algorithm Based on Trust." In 2018 Eighth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC). IEEE, 2018. http://dx.doi.org/10.1109/imccc.2018.00172.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Jing-xia, Ren, and Wu Zhi-feng. "Collaborative Filtering Algorithm Based on Dynamic Trust Attenuation." In ICBDT 2020: 2020 3rd International Conference on Big Data Technologies. ACM, 2020. http://dx.doi.org/10.1145/3422713.3422727.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Chen, Xinxin, Yu Guo, Yang Yang, and Zhenqiang Mi. "Trust-based collaborative filtering algorithm in social network." In 2016 International Conference on Computer, Information and Telecommunication Systems (CITS). IEEE, 2016. http://dx.doi.org/10.1109/cits.2016.7546412.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Moghaddam, Morteza Ghorbani, Norwati Mustapha, Aida Mustapha, Nurfadhlina Mohd Sharef, and Anousheh Elahian. "AgeTrust: A New Temporal Trust-Based Collaborative Filtering Approach." In 2014 International Conference on Information Science and Applications (ICISA). IEEE, 2014. http://dx.doi.org/10.1109/icisa.2014.6847352.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!