Journal articles: 'Trust-based collaborative filtering'

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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.

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Ngwawe, Edwin, Elisha Abade, and Stephen Mburu. "Trust Enhanced Collaborative Filtering Recommendation Algorithm." International Research Journal of Computer Science 10, no. 04 (May 31, 2023): 88–96. http://dx.doi.org/10.26562/irjcs.2023.v1004.10.

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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.

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Kim, Hyoung Do. "Applying Consistency-Based Trust Definition to Collaborative Filtering." KSII Transactions on Internet and Information Systems 3, no. 4 (August 30, 2009): 366–75. http://dx.doi.org/10.3837/tiis.2009.04.002.

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Duan, Miao. "Collaborative Filtering Recommendation Algorithm based on Trust Propagation." International Journal of Security and Its Applications 9, no. 7 (July 31, 2015): 99–108. http://dx.doi.org/10.14257/ijsia.2015.9.7.09.

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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 (July 14, 2018): 113–35. http://dx.doi.org/10.1007/s10844-018-0517-4.

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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 (March 15, 2017): 297–307. http://dx.doi.org/10.1007/s41060-017-0049-y.

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Yuan, Zahir, and Yang. "Modeling Implicit Trust in Matrix Factorization-Based Collaborative Filtering." Applied Sciences 9, no. 20 (October 16, 2019): 4378. http://dx.doi.org/10.3390/app9204378.

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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).

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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 (August 2011): 587–601. http://dx.doi.org/10.1016/j.ijhcs.2011.06.001.

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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.

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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.

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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.

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Huang, Wenjun, Junyu Chen, and Yue Ding. "Research on Collaborative Filtering Recommendation Based on Trust Relationship and Rating Trust." Frontiers in Business, Economics and Management 1, no. 2 (April 19, 2021): 1–9. http://dx.doi.org/10.54097/fbem.v1i2.13.

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In the Internet age, how to dig out useful information from massive data has become a research hotspot. The emergence of recommendation algorithms effectively solves the problem of information overload, but traditional recommendation algorithms face problems such as data sparseness, cold start, and low accuracy. Later social recommendation algorithms usually only use a single social trust information for recommendation, and the integration of multiple trust relationships lacks an efficient model, which greatly affects the accuracy and reliability of recommendation. This paper proposes a trust-based approach. Recommended algorithm. First, use social trust data to calculate user trust relationships, including user local trust and user global trust. Further based on the scoring data, an implicit trust relationship is calculated, called rating trust, which includes scoring local trust and scoring global trust. Then set the recommendation weight, build the preference relationship between users through user trust and rating trust, and form a comprehensive trust relationship. The trust relationship of social networks is integrated into the probability matrix decomposition model to form an efficient and unified trusted recommendation model TR-PMF. This algorithm is compared with related algorithms on the Ciao and FilmTrust datasets, and the results prove that our method is competitive with other recommendation algorithms.

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Songjie Gong. "A Collaborative Filtering Recommendation Algorithm Based on Trust Network and Trust Factor." Journal of Convergence Information Technology 8, no. 5 (March 15, 2013): 1111–18. http://dx.doi.org/10.4156/jcit.vol8.issue5.129.

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Tan, Chengfang, Lin Cui, and Xiaoyin Wu. "Fuzzy trust based collaborative filtering analysis for mobile user preferences." Journal of Intelligent & Fuzzy Systems 40, no. 4 (April 12, 2021): 8269–75. http://dx.doi.org/10.3233/jifs-189649.

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With the rapid development of mobile terminal devices, mobile user activities can be carried out anytime and anywhere through various mobile terminals. The current research on mobile communication network is mainly focused on extracting useful and interesting information for mobile user from massive and disordered information. However, the sparsity of scoring data matrix results in low quality of recommendation algorithm. In order to overcome this drawback, the traditional collaborative filtering algorithm is improved. First, the user-interest matrix and item-feature matrix were obtained by analyzing mobile user behavior and item attributes. Fuzzy trust based model is utilized for collaborative filtering analysis for mobile user preferences. Then, the similarity between different mobile users was calculated by weighted calculation. With this method, mobile user preference can be predicted effectively, making it possible to recommend rational resource and waste less time in extracting resources out of the massive information. Experimental results show that the proposed algorithm reduces the mean absolute error (MAE) and the impact of sparse scoring matrix data compared with the traditional collaborative filtering algorithm, and improves the recommendation effect to a certain extent.

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Meng, Weizhi, Wenjuan Li, and Lam For Kwok. "Towards Effective Trust-Based Packet Filtering in Collaborative Network Environments." IEEE Transactions on Network and Service Management 14, no. 1 (March 2017): 233–45. http://dx.doi.org/10.1109/tnsm.2017.2664893.

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Sharma, Sanjeev Kumar, and Ugrasen Suman. "A trust-based architectural framework for collaborative filtering recommender system." International Journal of Business Information Systems 16, no. 2 (2014): 134. http://dx.doi.org/10.1504/ijbis.2014.062835.

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Gou, Jin, Junjie Guo, Lu Zhang, and Cheng Wang. "Collaborative filtering recommendation system based on trust-aware and domain experts." Intelligent Data Analysis 23 (June 27, 2019): 133–51. http://dx.doi.org/10.3233/ida-192531.

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Ye, Li, Chunming Wu, and Min Li. "Collaborative Filtering Recommendation Based on Trust Model with Fused Similar Factor." MATEC Web of Conferences 139 (2017): 00010. http://dx.doi.org/10.1051/matecconf/201713900010.

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Jiang, Liaoliang, Yuting Cheng, Li Yang, Jing Li, Hongyang Yan, and Xiaoqin Wang. "A trust-based collaborative filtering algorithm for E-commerce recommendation system." Journal of Ambient Intelligence and Humanized Computing 10, no. 8 (June 29, 2018): 3023–34. http://dx.doi.org/10.1007/s12652-018-0928-7.

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Ma, Xiao, Hongwei Lu, Zaobin Gan, and Jiangfeng Zeng. "An explicit trust and distrust clustering based collaborative filtering recommendation approach." Electronic Commerce Research and Applications 25 (September 2017): 29–39. http://dx.doi.org/10.1016/j.elerap.2017.06.005.

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Parvin, Hashem, Parham Moradi, and Shahrokh Esmaeili. "TCFACO: Trust-aware collaborative filtering method based on ant colony optimization." Expert Systems with Applications 118 (March 2019): 152–68. http://dx.doi.org/10.1016/j.eswa.2018.09.045.

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Song, Jiagang, Jiayu Song, Xinpan Yuan, Xiao He, and Xinghui Zhu. "Graph Representation-Based Deep Multi-View Semantic Similarity Learning Model for Recommendation." Future Internet 14, no. 2 (January 19, 2022): 32. http://dx.doi.org/10.3390/fi14020032.

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With the rapid development of Internet technology, how to mine and analyze massive amounts of network information to provide users with accurate and fast recommendation information has become a hot and difficult topic of joint research in industry and academia in recent years. One of the most widely used social network recommendation methods is collaborative filtering. However, traditional social network-based collaborative filtering algorithms will encounter problems such as low recommendation performance and cold start due to high data sparsity and uneven distribution. In addition, these collaborative filtering algorithms do not effectively consider the implicit trust relationship between users. To this end, this paper proposes a collaborative filtering recommendation algorithm based on graphsage (GraphSAGE-CF). The algorithm first uses graphsage to learn low-dimensional feature representations of the global and local structures of user nodes in social networks and then calculates the implicit trust relationship between users through the feature representations learned by graphsage. Finally, the comprehensive evaluation shows the scores of users and implicit users on related items and predicts the scores of users on target items. Experimental results on four open standard datasets show that our proposed graphsage-cf algorithm is superior to existing algorithms in RMSE and MAE.

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Wu, Li Hua, and Wen Feng Chen. "Personalized Recommendation Based on Trust and Preference." Applied Mechanics and Materials 713-715 (January 2015): 2288–91. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.2288.

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Collaborative filtering recommendation is a mainstream personalized recommendation method, which has some flaws in actual application. And there are some inaccurate recommendation results in some cases. Considering the relationship of trust and similarity of user preference, this paper introduces trust to recommendation model and considers multi-dimensional factors of user preference, proposes a personalized recommendation method based on trust and preference. The recommendation method can improve the accuracy of recommendation system. Finally, this paper proves effectiveness of this recommendation method through the experiments.

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Hriekes, EEva Diab, and Yosser AlSayed Souleiman AlAtassi. "Improve the Performance of Advice Systems Based on Cooperative Liquidation Using Trust Relationships." JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences 27, no. 1 (April 1, 2019): 87–106. http://dx.doi.org/10.29196/jubpas.v27i1.2068.

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Recommender systems are one of the recent inventions to deal with information overload problem and provide users with personalized recommendations that may be of their interests. Collaborative filtering is the most popular and widely used technique to build recommender systems and has been successfully employed in many applications. However, collaborative filtering suffers from several inherent issues that affect the recommendation accuracy such as: data sparsity and cold start problems caused by the lack of user ratings, so the recommendation results are often unsatisfactory. To address these problems, we propose a recommendation method called “MFGLT” that enhance the recommendation accuracy of collaborative filtering method using trust-based social networks by leveraging different user's situations (as a trustor and as a trustee) in these networks to model user preferences. Specifically, we propose model-based method that uses matrix factorization technique and exploit both local social context represented by modeling explicit user interactions and implicit user interactions with other users, and also the global social context represented by the user reputation in the whole social network for making recommendations. Experimental results based on real-world dataset demonstrate that our approach gives better performance than the other trust-aware recommendation approaches, in terms of prediction accuracy.

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Zhang, Yao, Shuangliang Tai, and Kunhui Ye. "Contractor Recommendation Model Using Credit Networking and Collaborative Filtering." Buildings 12, no. 12 (November 22, 2022): 2049. http://dx.doi.org/10.3390/buildings12122049.

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The credit of contractors in the construction market directly affects the cooperative intentions of owners. Although previous scholars have attempted to use credit to select appropriate contractors, they have rarely considered the trust relationship between decision-making and former owners. This work introduces and verifies a credit network recommendation model based on a collaborative filtering algorithm. The contractor’s credit established based on this model serves as a viable method for owners to select efficient contractors. The application of the model includes relevant information collection, neighbor set formation, contractor’s credit evaluation, and recommendation list formation, among which the neighbor set of the owner is used to calculate the comprehensive trust degree of the decision-making owner to the former owner. A time decay function is adopted to correct the difference in the trust relationship between an owner and a contractor introduced over time. To verify the feasibility of this model, an actual scenario was simulated, and the results obtained via simulations were compared and found to be consistent. Thus, a contractor with a high credit can be recommended to the decision-making owner. This approach is crucial for promoting contractors’ credit and conducive to the healthy development of the construction market.

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Zahir, Yuan, and Moniz. "AgreeRelTrust—a Simple Implicit Trust Inference Model for Memory-Based Collaborative Filtering Recommendation Systems." Electronics 8, no. 4 (April 11, 2019): 427. http://dx.doi.org/10.3390/electronics8040427.

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Recommendation systems alleviate the problem of information overload by helping users find information relevant to their preference. Memory-based recommender systems use correlation-based similarity to measure the common interest among users. The trust between users is often used to address the issues associated with correlation-based similarity measures. However, in most applications, the trust relationships between users are not available. A popular method to extract the implicit trust relationship between users employs prediction accuracy. This method has several problems such as high computational cost and data sparsity. In this paper, addressing the problems associated with prediction accuracy-based trust extraction methods, we proposed a novel trust-based method called AgreeRelTrust. Unlike accuracy-based methods, this method does not require the calculation of initial prediction and the trust relationship is more meaningful. The collective agreements between any two users and their relative activities are fused to obtain the trust relationship. To evaluate the usefulness of our method, we applied it to three public data sets and compared the prediction accuracy with well-known collaborative filtering methods. The experimental results show our method has large improvements over the other methods.

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Kim, Kyung Soo, Doo Soo Chang, and Yong Suk Choi. "Boosting Memory-Based Collaborative Filtering Using Content-Metadata." Symmetry 11, no. 4 (April 18, 2019): 561. http://dx.doi.org/10.3390/sym11040561.

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Recommendation systems are widely used in conjunction with many popular personalized services, which enables people to find not only content items they are currently interested in, but also those in which they might become interested. Many recommendation systems employ the memory-based collaborative filtering (CF) method, which has been generally accepted as one of consensus approaches. Despite the usefulness of the CF method for successful recommendation, several limitations remain, such as sparsity and cold-start problems that degrade the performance of CF systems in practice. To overcome these limitations, we propose a content-metadata-based approach that uses content-metadata in an effective way. By complementarily combining content-metadata with conventional user-content ratings and trust network information, our proposed approach remarkably increases the amount of suggested content and accurately recommends a large number of additional content items. Experimental results show a significant enhancement of performance, especially under a sparse rating environment.

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吴, 应良. "An Improved Collaborative Filtering Recommendation Model and Method Based on Social Trust." E-Commerce Letters 08, no. 02 (2019): 63–73. http://dx.doi.org/10.12677/ecl.2018.82008.

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Mohammed Ismail, Dr, Dr K. Bhanu Prakash, and Dr M. Nagabhushana Rao. "Collaborative filtering-based recommendation of online social voting." International Journal of Engineering & Technology 7, no. 3 (July 16, 2018): 1504. http://dx.doi.org/10.14419/ijet.v7i3.11630.

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Social voting is becoming the new reason behind social recommendation these days. It helps in providing accurate recommendations with the help of factors like social trust etc. Here we propose Matrix factorization (MF) and nearest neighbor-based recommender systems accommodating the factors of user activities and also compared them with the peer reviewers, to provide a accurate recommendation. Through experiments we realized that the affiliation factors are very much needed for improving the accuracy of the recommender systems. This information helps us to overcome the cold start problem of the recommendation system and also y the analysis this information was much useful to cold users than to heavy users. In our experiments simple neighborhood model outperform the computerized matrix factorization models in the hot voting and non hot voting recommendation. We also proposed a hybrid recommender system producing a top-k recommendation inculcating different single approaches.

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Roy, Falguni, and Mahamudul Hasan. "Comparative Analysis of Different Trust Metrics of User-User Trust-Based Recommendation System." Computer Science 23, no. 3 (October 2, 2022): 337–75. http://dx.doi.org/10.7494/csci.2022.23.3.4227.

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Information overload is the biggest challenge nowadays for any website, especially e-commerce websites. However, this challenge arises for the fast growth of information on the web (WWW) with easy access to the internet. Collaborative filtering based recommender system is the most useful application to solve the information overload problem by filtering relevant information for the users according to their interests. But, the existing system faces some significant limitations such as data sparsity, low accuracy, cold-start, and malicious attacks. To alleviate the mentioned issues, the relationship of trust incorporates in the system where it can be between the users or items, and such system is known as the trust-based recommender system (TBRS). From the user perspective, the motive of the TBRS is to utilize the reliability between the users to generate more accurate and trusted recommendations. However, the study aims to present a comparative analysis of different trust metrics in the context of the type of trust definition of TBRS. Also, the study accomplishes twenty-four trust metrics in terms of the methodology, trust properties \& measurement, validation approaches, and the experimented dataset.

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Martinez-Pabon, Francisco, Juan Camilo Ospina-Quintero, Gustavo Ramirez-Gonzalez, and Mario Munoz-Organero. "Recommending Ads from Trustworthy Relationships in Pervasive Environments." Mobile Information Systems 2016 (2016): 1–18. http://dx.doi.org/10.1155/2016/8593173.

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The use of pervasive computing technologies for advertising purposes is an interesting emergent field for large, medium, and small companies. Although recommender systems have been a traditional solution to decrease users’ cognitive effort to find good and personalized items, the classic collaborative filtering needs to include contextual information to be more effective. The inclusion of users’ social context information in the recommendation algorithm, specifically trust in other users, may be a mechanism for obtaining ads’ influence from other users in their closest social circle. However, there is no consensus about the variables to use during the trust inference process, and its integration into a classic collaborative filtering recommender system deserves a deeper research. On the other hand, the pervasive advertising domain demands a recommender system evaluation from a novelty/precision perspective. The improvement of the precision/novelty balance is not only a matter related to the recommendation algorithm itself but also a better recommendations’ display strategy. In this paper, we propose a novel approach for a collaborative filtering recommender system based on trust, which was tested throughout a digital signage prototype using a multiscreen scheme for recommendations delivery to evaluate our proposal using a novelty/precision approach.

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Zuo, Long, Shuo Xiong, Xin Qi, Zheng Wen, and Yiwen Tang. "Communication-Based Book Recommendation in Computational Social Systems." Complexity 2021 (January 29, 2021): 1–10. http://dx.doi.org/10.1155/2021/6651493.

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This paper considers current personalized recommendation approaches based on computational social systems and then discusses their advantages and application environments. The most widely used recommendation algorithm, personalized advice based on collaborative filtering, is selected as the primary research focus. Some improvements in its application performance are analyzed. First, for the calculation of user similarity, the introduction of computational social system attributes can help to determine users’ neighbors more accurately. Second, computational social system strategies can be adopted to penalize popular items. Third, the network community, identity, and trust can be combined as there is a close relationship. Therefore, this paper proposes a new method that uses a computational social system, including a trust model based on community relationships, to improve the user similarity calculation accuracy to enhance personalized recommendation. Finally, the improved algorithm in this paper is tested on the online reading website dataset. The experimental results show that the enhanced collaborative filtering algorithm performs better than the traditional algorithm.

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Zhimin Chen, Yi Jiang, and Yao Zhao. "A Collaborative Filtering Recommendation Algorithm Based on User Interest Change and Trust Evaluation." International Journal of Digital Content Technology and its Applications 4, no. 9 (December 31, 2010): 106–13. http://dx.doi.org/10.4156/jdcta.vol4.issue9.13.

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Lai, Chin-Hui, Duen-Ren Liu, and Cai-Sin Lin. "Novel personal and group-based trust models in collaborative filtering for document recommendation." Information Sciences 239 (August 2013): 31–49. http://dx.doi.org/10.1016/j.ins.2013.03.030.

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He, Wei. "Interior Design Scheme Recommendation Method Based on Improved Collaborative Filtering Algorithm." Wireless Communications and Mobile Computing 2021 (December 23, 2021): 1–10. http://dx.doi.org/10.1155/2021/3834550.

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The recommendation engine is similar to the function of the product recommender in our real life, which provides great convenience for people to choose the appropriate decoration scheme in the process of interior design and decoration. A home improvement website or company can design a suitable recommendation algorithm to provide home improvement program recommendation services for users with decoration needs. After understanding the user behavior of the home decoration website, this paper proposes an interior design scheme recommendation method based on an improved collaborative filtering algorithm. The method designs a collaborative filtering algorithm that combines multilayer hybrid similarity and trust mechanisms. Fuzzy set membership function is introduced to correct users’ rating similarity, and users’ interest vector is extracted to calculate users’ preference for different types of items. The algorithm dynamically fuses those two aspects to obtain the mixed similarity of users; meanwhile, the user’s hybrid similarity and trust are fused in an adaptive model. Then, the user neighbor data set generated based on the overall similarity of users is used as a training set, taking the item scores and features into consideration. On the one hand, the users and the projects are taken into account as well. The final prediction score is more accurate, and the recommendation effect is better. The experimental results show that this method can recommend interior design schemes with high performance, and its performance is better than other methods.

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Chaomeng, Gao, and Wang Yonggang. "Analysis of Brand Visual Design Based on Collaborative Filtering Algorithm." Discrete Dynamics in Nature and Society 2022 (January 13, 2022): 1–8. http://dx.doi.org/10.1155/2022/8235966.

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With the continuous development of China’s social economy, the competitiveness of brand market is gradually increasing. In order to improve their own level in brand building, major enterprises gradually explore and study visual communication design. Brand visual design has also received more and more attention. Building a complete and rich visual design system can improve the brand level and attract users to consume. Based on the abovementioned situation, this paper proposes to use collaborative filtering algorithm to analyze and study brand visual design. Firstly, a solution is proposed to solve the problem of low accuracy of general recommendation algorithm in brand goods. Collaborative filtering algorithm is used to analyze the visual communication design process of enterprise brand. Research on personalized image design according to consumers’ trust and recognition of brand design is conducted. In traditional craft brand visual design, we mainly study the impact of image design on consumer behavior. The brand loyalty model is used to predict and analyze the visual design effect. Also, the user’s evaluation coefficient is taken as the expression of brand visual design recognition. Finally, the collaborative filtering algorithm is optimized to improve the consumer similarity based on the original algorithm. The results show that the brand visual design using collaborative filtering algorithm can help enterprises obtain greater benefits in their own brand construction. It provides effective data help in the development of traditional craft brands.

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Sobha Rani, K. "TrustSVD: A Novel Trust-Based Matrix Factorization Model with User Trust and Item Ratings." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 11 (November 30, 2017): 7. http://dx.doi.org/10.23956/ijarcsse.v7i11.422.

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Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.

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Ghodousi, Elnaz, and Ali Hamzeh. "A New Approach for Trust Prediction by using collaborative filtering based of Pareto dominance in Social Networks." Ciência e Natura 37 (December 19, 2015): 95. http://dx.doi.org/10.5902/2179460x20758.

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Along with the increasing popularity of social web sites, users rely more on the trustworthiness informationfor many online activities among users.[24] However, such social network data often suffers from two problems,(1)severe data sparsity and are not able to provide users with enough information, (2)dataset’s is very large.Therefore, trust prediction has emerged as an important topic in social network research. In this paper weproposed a new approach by using collaborative filtering method and the concept of Pareto dominance. We usesPareto dominance to perform a pre-filtering process eliminating less representative users from the k-neighbourselection process while retaining the most promising ones. The results from experiments performed on FilmTrustdataset and Epinions dataset.

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Banda, Latha, Karan Singh, Le Hoang Son, Mohamed Abdel-Basset, Pham Huy Thong, Hiep Xuan Huynh, and David Taniar. "Recommender Systems Using Collaborative Tagging." International Journal of Data Warehousing and Mining 16, no. 3 (July 2020): 183–200. http://dx.doi.org/10.4018/ijdwm.2020070110.

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Collaborative tagging is a useful and effective way for classifying items with respect to search, sharing information so that users can be tagged via online social networking. This article proposes a novel recommender system for collaborative tagging in which the genre interestingness measure and gradual decay are utilized with diffusion similarity. The comparison has been done on the benchmark recommender system datasets namely MovieLens, Amazon datasets against the existing approaches such as collaborative filtering based on tagging using E-FCM, and E-GK clustering algorithms, hybrid recommender systems based on tagging using GA and collaborative tagging using incremental clustering with trust. The experimental results ensure that the proposed approach achieves maximum prediction accuracy ratio of 9.25% for average of various splits data of 100 users, which is higher than the existing approaches obtained only prediction accuracy of 5.76%.

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O'DONOVAN, JOHN, and BARRY SMYTH. "MINING TRUST VALUES FROM RECOMMENDATION ERRORS." International Journal on Artificial Intelligence Tools 15, no. 06 (December 2006): 945–62. http://dx.doi.org/10.1142/s0218213006003053.

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Increasing availability of information has furthered the need for recommender systems across a variety of domains. These systems are designed to tailor each user's information space to suit their particular information needs. Collaborative filtering is a successful and popular technique for producing recommendations based on similarities in users' tastes and opinions. Our work focusses on these similarities and the fact that current techniques for defining which users contribute to recommendation are in need of improvement. In this paper we propose the use of trustworthiness as an improvement to this situation. In particular, we define and empirically test a technique for eliciting trust values for each producer of a recommendation based on that user's history of contributions to recommendations. We compute a recommendation range to present to a target user. This is done by leveraging under/overestimate errors in users' past contributions in the recommendation process. We present three different models to compute this range. Our evaluation shows how this trust-based technique can be easily incorporated into a standard collaborative filtering algorithm and we define a fair comparison in which our technique outperforms a benchmark algorithm in predictive accuracy. We aim to show that the presentation of absolute rating predictions to users is more likely to reduce user trust in the recommendation system than presentation of a range of rating predictions. To evaluate the trust benefits resulting from the transparency of our recommendation range techniques, we carry out user-satisfaction trials on BoozerChoozer, a pub recommendation system. Our user-satisfaction results show that the recommendation range techniques perform up to twice as well as the benchmark.

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Paul, P. Mano, and R. Ravi. "A Collaborative Reputation-Based Vector Space Model for Email Spam Filtering." Journal of Computational and Theoretical Nanoscience 15, no. 2 (February 1, 2018): 474–79. http://dx.doi.org/10.1166/jctn.2018.7128.

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In this paper, we propose a novel Collaborative Reputation-based Vector Space Model (CRVSM) for detection of spam email. CRVSM uses a vector space model for representing the feature vectors in multidimensional vector space in order to detect the spam emails in large space. We cluster the emails into five clusters so as to reduce the email spam detection time. To reduce the number of false positives and false negatives, we calculate maximum similarity measure with maximum and minimum threshold range. Moreover we use a reputation evaluation function which determines the reporter's trust level in validating an email as spam or non-spam. The CRVSM approach achieves good efficiency while obtaining good reputation result in Email spam detection. The performance of CRVSM model has been evaluated using metrics such as false positive rate, false negative rate, detection accuracy and detection time. The performance results clearly show that CRVSM accurately detects the incoming emails as spam or non-spam with less FPR and FNR values thereby achieving a high efficiency with short detection time and outperforms the existing detection protocols.

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Chen, Chaochao, Xiaolin Zheng, Mengying Zhu, and Litao Xiao. "Recommender System with Composite Social Trust Networks." International Journal of Web Services Research 13, no. 2 (April 2016): 56–73. http://dx.doi.org/10.4018/ijwsr.2016040104.

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The development of online social networks has increased the importance of social recommendations. Social recommender systems are based on the idea that users who are linked in a social trust network tend to share similar interests. Thus, how to build an accurate social trust network will greatly affect recommendation performance. However, existing trust-based recommender approaches do not fully utilize social information to build rational trust networks and thus have low prediction accuracy and slow convergence speed. In this paper, the authors propose a composite trust-based probabilistic matrix factorization model, which is mainly composed of two steps: In step 1, the existing explicit trust network and the inferred implicit trust network are used to build a composite trust network. In step 2, the composite trust network is used to minimize both the rating difference and the trust difference between the true value and the inferred value. Experiments based on an Epinions dataset show that the authors' approach has significantly higher prediction accuracy and convergence speed than traditional collaborative filtering technology and the state-of-the-art trust-based recommendation approaches.

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F, Mary Harin Fernandez, Ramya S, and Revathy V. "Social Recommendation Model with User Trust and Item Ratings Using Collaborative Filtering Technique in Hotel Application." Informatica : Journal of Applied Machines Electrical Electronics Computer Science and Communication Systems 01, no. 01 (December 1, 2020): 17–22. http://dx.doi.org/10.47812/ijamecs2010103.

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A trust-based recommendation model is regularized with user trust and item ratings called TrustSVD. Trust networks are large-world networks where many users are socially linked, suggesting the assumption of trust in recommendation systems. An item rating downloaded from the OSN Server can be viewed by the user. If the information is accessible on the server, all the adjacent devices are enabled and a peer to peer mode of communication is initiated. User reviews from a graphical forum are shown. It focuses on the rating prediction role in the current framework and has shown that integrating user social confidence data will boost the output of recommendations. The strategy builds on the SVD++ state-of-the-art model. The data sparsity and cold start issues are resolved in the friend of friend recommendation model used. The mining method generates the user's overall rating in graphical representations and illustrates the overall rating. This model increases the utility of data by exchanging neighborhoods to protect security and privacy issues. One of the most common techniques for implementing a recommendation scheme is Collaborative filtering (CF).

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Victor, Patricia, Chris Cornelis, Martine De Cock, and Ankur Teredesai. "A Comparative Analysis of Trust-Enhanced Recommenders for Controversial Items." Proceedings of the International AAAI Conference on Web and Social Media 3, no. 1 (March 20, 2009): 342–45. http://dx.doi.org/10.1609/icwsm.v3i1.13986.

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A particularly challenging task for recommender systems (RSs) is deciding whether to recommend an item that received a variety of high and low scores from its users. RSs that incorporate a trust network among their users have the potential to make more personalized recommendations for such controversial items (CIs) compared to collaborative filtering (CF) based systems, provided they succeed in utilizing the trust information to their advantage. In this paper, we formalize the concept of CIs in RSs. We then compare the performance of several well-known trust-enhanced techniques for effectively personalizing the recommendations for CIs versus random items in the RS. Furthermore, we introduce a new algorithm that maximizes the synergy between CF and its trust-based variants, and show that the new algorithm outperforms other trust-based techniques in generating rating predictions for CIs.

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Lai, Chin-Hui, and Yu-Chieh Chang. "Document recommendation based on the analysis of group trust and user weightings." Journal of Information Science 45, no. 6 (January 4, 2019): 845–62. http://dx.doi.org/10.1177/0165551518819973.

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Collaborative filtering (CF) has been applied in various domains to resolve problems related to information overload. In a knowledge-intensive environment, most works are processed through teamwork. A user on a team can reference task-related documents from other trusted members to support work on the task. However, the traditional personalised recommender systems no longer meet the demand of teams or groups. Therefore, this work proposes a novel document recommendation method based on a group-based trust model. Our method will analyse the degrees of trust among users in a group and then identify the trustworthy users. The proposed group trust consists of a hybrid personal trust (HPT) model and users’ importance (i.e. users’ activity, similarity and reputation) in a group. Group-based trust is then integrated with the user-based CF to recommend documents to users. The experiments demonstrate that the proposed method can provide better performance than other trust-based recommendation methods; it not only obtains reliable trust values to increase the accuracy of predictions but also enhances the recommendation quality.

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Zhang, Shuai, Wenting Yang, Song Xu, and Wenyu Zhang. "A Hybrid Social Network-based Collaborative Filtering Method for Personalized Manufacturing Service Recommendation." International Journal of Computers Communications & Control 12, no. 5 (September 10, 2017): 728. http://dx.doi.org/10.15837/ijccc.2017.5.2930.

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Nowadays, social network-based collaborative filtering (CF) methods are widely applied to recommend suitable products to consumers by combining trust relationships and similarities in the preference ratings among past users. However, these types of methods are rarely used for recommending manufacturing services. Hence, this study has developed a hybrid social network-based CF method for recommending personalized manufacturing services. The trustworthy enterprises and three types of similar enterprises with different features were considered as the four influential components for calculating predicted ratings of candidate services. The stochastic approach for link structure analysis (SALSA) was adopted to select top K trustworthy enterprises while also considering their reputation propagation on enterprise social network. The predicted ratings of candidate services were computed by using an extended user-based CF method where the particle swarm optimization (PSO) algorithm was leveraged to optimize the weights of the four components, thus making service recommendation more objective. Finally, an evaluation experiment illustrated that the proposed method is more accurate than the traditional user-based CF method.

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Hsu, Ping-Yu, Jui-Yi Chung, and Yu-Chin Liu. "Using the beta distribution technique to detect attacked items from collaborative filtering." Intelligent Data Analysis 25, no. 1 (January 26, 2021): 121–37. http://dx.doi.org/10.3233/ida-194935.

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A recommendation system is based on the user and the items, providing appropriate items to the user and effectively helping the user to find items that may be of interest. The most commonly used recommendation method is collaborative filtering. However, in this case, the recommendation system will be injected with false data to create false ratings to push or nuke specific items. This will affect the user’s trust in the recommendation system. After all, it is important that the recommendation system provides a trusted recommendation item. Therefore, there are many algorithms for detecting attacks. In this article, it proposes a method to detect attacks based on the beta distribution. Different researchers in the past assumed that the attacker only attacked one target item in the user data. This research simulated an attacker attacking multiple target items in the experiment. The result showed a detection rate of more than 80%, and the false rate was within 16%.

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Xu, Chonghuan. "Personal Recommendation Using a Novel Collaborative Filtering Algorithm in Customer Relationship Management." Discrete Dynamics in Nature and Society 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/739460.

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With the rapid development of customer relationship management, more and more user recommendation technologies are used to enhance the customer satisfaction. Although there are many good recommendation algorithms, it is still a challenge to increase the accuracy and diversity of these algorithms to fulfill users’ preferences. In this paper, we construct a user recommendation model containing a new method to compute the similarities among users on bipartite networks. Different from other standard similarities, we consider the influence of each object node including popular degree, preference degree, and trust relationship. Substituting these new definitions of similarity for the standard cosine similarity, we propose a modified collaborative filtering algorithm based on multifactors (CF-M). Detailed experimental analysis on two benchmark datasets shows that the CF-M is of high accuracy and also generates more diversity.

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Zhang, Xuefeng, Xiuli Chen, Dewen Seng, and Xujian Fang. "A Factored Similarity Model with Trust and Social Influence for Top-N Recommendation." International Journal of Computers Communications & Control 14, no. 4 (August 5, 2019): 590–607. http://dx.doi.org/10.15837/ijccc.2019.4.3577.

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Many trust-aware recommendation systems have emerged to overcome the problem of data sparsity, which bottlenecks the performance of traditional Collaborative Filtering (CF) recommendation algorithms. However, these systems most rely on the binary social network information, failing to consider the variety of trust values between users. To make up for the defect, this paper designs a novel Top-N recommendation model based on trust and social influence, in which the most influential users are determined by the Improved Structural Holes (ISH) method. Specifically, the features in Matrix Factorization (MF) were configured by deep learning rather than random initialization, which has a negative impact on prediction of item rating. In addition, a trust measurement model was created to quantify the strength of implicit trust. The experimental result shows that our approach can solve the adverse impacts of data sparsity and enhance the recommendation accuracy.

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Sun, Dasong, Shuqing Li, Wenjing Yan, Fusen Jiao, and Junpeng Chen. "Research on User Interest Expression and Recommendation Service based on Three-dimensional Relationship of Users and Items." International Journal on Recent and Innovation Trends in Computing and Communication 8, no. 5 (May 31, 2020): 01–15. http://dx.doi.org/10.17762/ijritcc.v8i5.5382.

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The existing recommendation algorithms often rely heavily on the original score information in the user rating matrix. However, the user's rating of items does not fully reflect the user's real interest. Therefore, the key to improve the existing recommendation system algorithm effectively is to eliminate the influence of these unfavorable factors and the accuracy of the recommendation algorithm can be improved by correcting the original user rating information reasonably. This paper makes a comprehensive theoretical analysis and method design from three aspects: the quality of the item, the memory function of the user and the influence of the social friends trusted by the user on the user's rating. Based on these methods, this paper finally proposes a collaborative filtering recommendation algorithm (FixCF) based on user rating modification. Using data sets such as Movielens, Epinions and Flixster, the data sets are divided into five representative subsets, and the experimental demonstration is carried out. FixCF and classical collaborative filtering algorithms, existing matrix decomposition-based algorithms and trust network-based inference are compared. The experimental results show that the accuracy and coverage of FixCF have been improved under many experimental conditions.

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Wu, Jian, Jiali Chang, Qingwei Cao, and Changyong Liang. "A trust propagation and collaborative filtering based method for incomplete information in social network group decision making with type-2 linguistic trust." Computers & Industrial Engineering 127 (January 2019): 853–64. http://dx.doi.org/10.1016/j.cie.2018.11.020.

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

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