A reliability-based recommendation method to improve trust-aware recommender systems

Moradi, Parham; Ahmadian, Sajad

doi:10.1016/j.eswa.2015.05.027

Cited by 176 publications

(109 citation statements)

References 45 publications

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“…Güven, satıcının kendi çıkarları yanında müşterisinin çıkarlarını düşünme, kullanıcıya verilen taahhütleri yerine getirme, müşteriyi kandırmama ve satıcının müşterinin isteklerinin yerine getirebilme yetenek ve bilgisine sahip olma ile ilgili müşterinin beklentilerinin toplamıdır [18,16]. Özellikle eticarette alıcı-satıcı arasındaki ilişkide güven çok önemlidir [19]. Güven, potansiyel müşterilerin söz konusu internet sitesinde daha fazla vakit geçirmelerini, internet sitesinin sayfaları arasında dolaşmalarını, siteye katkıda bulunmalarını (yorum vb.)…”

Section: Güvenunclassified

Fırsat Sitelerinden Tekrar Satın Alma Kararını Etkileyen Faktörlerin Araştırılması

Gümüşsoy¹,

Yeterel²

2016

BTD

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Özet-Fırsat siteleri, işletmelerin ürün ve hizmetlerini, müşterilerine indirimli kuponlar halinde sunmalarını sağlayan sanal ortamlar olarak tanımlanabilir. Fırsat sitelerinin çeşitliliği ve sayısının artması ile birlikte müşteriler artık aynı ürün/hizmeti alternatif bir web sitesinden edinebilmektedirler. Oysaki firmaların varlıklarını sürdürmelerinde var olan müşteriyi korumak yeni bir müşteri edinmeye göre daha az maliyetlidir. Bu çalışmanın amacı, fırsat sitelerinden alışveriş yapan müşterilerin ilgili web sitesinden tekrar alışveriş yapmaya karar vermelerinde etkili olan faktörlerin araştırılmasıdır. Bunun için, memnuniyet, algılanan fayda, algılanan kullanım kolaylığı, kişisel normlar, algılanan saygınlık ve güven faktörlerini içeren bir araştırma modeli oluşturulmuştur. Araştırma modeli, fırsat sitelerinin aktif kullanıcılarından toplanan anketler kullanılarak test edilmiştir. Analiz için yapısal eşitlik modellemesi kullanılmıştır. Sonuçlara göre güven ve memnuniyet faktörleri tekrar satın alma kararını etkilemektedir. Memnuniyet ise güven, algılanan fayda ve algılanan kullanım kalaylığı tarafından açıklanmaktadır. Kişisel normların, algılanan fayda üzerinde anlamlı bir etkisi bulunmasına rağmen tekrar satın alma niyeti üzerindeki etkisi anlamlı bulunmamıştır. Çalışmanın sonunda, çalışmanın sonuçları ve ileride yapılabilecek çalışmalar üzerinde durulmuştur.Anahtar Kelimeler-Fırsat sitesi; tekrar satın alma niyeti; güven; memnuniyet; algılanan fayda, algılanan kullanım kolaylığı; algılanan saygınlık; kişisel normlar Examining the Factors Affecting the Decision to Repurchase Intention from Group-Buying WebsitesAbstract-Group-buying websites are the virtual environments that the firms can sell products or services to their customers using coupons that offer price discounts. For the continuity of the firms, maintaining current customers is more important and cost effective than finding new customers. A research model is developed to identify the determinants of reuse intention of group-buying websites. To do this, the research model is constructed including the variables-satisfaction, perceived usefulness, perceived ease of use, subjective norms, perceived reputation and trust. The measurement and structural model are tested using the data, collected from the actual users of group-buying websites. In the analysis, LISREL 8.54 software is used. According to the results, trust and satisfaction are the predictors of repurchase intention. Satisfaction is explained with the variables trust, perceived usefulness and perceived ease of use. Although subjective norm has a significant effect on perceived usefulness its effect is found to be insignificant on repurchase intention. At the conclusion, the results and possible future studies are discussed.

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Section: Güvenunclassified

Fırsat Sitelerinden Tekrar Satın Alma Kararını Etkileyen Faktörlerin Araştırılması

Gümüşsoy¹,

Yeterel²

2016

BTD

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“…Guo et al [26] merged a user's trusted neighbors' ratings to represent the preferences of the users. Moradi and Ahmadian [27] proposed a reliability-based recommendation method to improve trustaware recommender systems. Jin et al [28] designed a trustbased top-k recommender system, which computed the trust values between users from users' interest similarity.…”

Section: Related Workmentioning

confidence: 99%

Using Trust of Social Ties for Recommendation

Chen

Shao

Zhu

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYNowadays, with the development of online social networks (OSN), a mass of online social information has been generated in OSN, which has triggered research on social recommendation. Collaborative filtering, as one of the most popular techniques in social recommendation, faces several challenges, such as data sparsity, cold-start users and prediction quality. The motivation of our work is to deal with the above challenges by effectively combining collaborative filtering technology with social information. The trust relationship has been identified as a useful means of using social information to improve the quality of recommendation. In this paper, we propose a trust-based recommendation approach which uses GlobalTrust (GT) to represent the trust value among users as neighboring nodes. A matrix factorization based on singular value decomposition is used to get a trust network built on the GT value. The recommendation results are obtained through a modified random walk algorithm called GlobalTrustWalker. Through experiments on a real-world sparser dataset, we demonstrate that the proposed approach can better utilize users' social trust information and improve the recommendation accuracy on coldstart users. key words: social network, trust-based, collaborative filtering, random walk

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“…For any two items i j and i k in the training set, slope one algorithm uses (2) to compute the mean deviation Md jk between them, where r uj − r uk is the difference between ratings of i j and i k by the same user u and |U jk (S)| = 0 is the number of users who rated i j and i k , and S is the set of all users. The target rating r vj of the user v on item j will be obtained finally from (3), where r vc is the rating of v to other items c, and the total number of the user's ratings |r vc | = 0. The recommendation follows the top-N technique.…”

Section: Collaborative Filteringmentioning

confidence: 99%

“…Each of these techniques has been applied in many RS applications, and they have worked with considerable success. However, research has shown that these traditional recommendation techniques have their shortcomings, such as sparsity and cold start problem, overspecialization, and so on [3,4]. More generally, using a single rating to make predictions is considered by the recommender systems research community as one of their great limitations [5].…”

Section: Introductionmentioning

confidence: 99%

A Neural Networks Approach for Improving the Accuracy of Multi-Criteria Recommender Systems

Hassan

Hamada

2017

Applied Sciences

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Accuracy improvement has been one of the most outstanding issues in the recommender systems research community. Recently, multi-criteria recommender systems that use multiple criteria ratings to estimate overall rating have been receiving considerable attention within the recommender systems research domain. This paper proposes a neural network model for improving the prediction accuracy of multi-criteria recommender systems. The neural network was trained using simulated annealing algorithms and integrated with two samples of single-rating recommender systems. The paper presents the experimental results for each of the two single-rating techniques together with their corresponding neural network-based models. To analyze the performance of the approach, we carried out a comparative analysis of the performance of each single rating-based technique and the proposed multi-criteria model. The experimental findings revealed that the proposed models have by far outperformed the existing techniques.

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A reliability-based recommendation method to improve trust-aware recommender systems

Cited by 176 publications

References 45 publications

Fırsat Sitelerinden Tekrar Satın Alma Kararını Etkileyen Faktörlerin Araştırılması

Fırsat Sitelerinden Tekrar Satın Alma Kararını Etkileyen Faktörlerin Araştırılması

Using Trust of Social Ties for Recommendation

A Neural Networks Approach for Improving the Accuracy of Multi-Criteria Recommender Systems

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