An effective recommender system by unifying user and item trust information for B2B applications

Shambour, Qusai; Lu, Jie

doi:10.1016/j.jcss.2014.12.029

Cited by 60 publications

(23 citation statements)

References 44 publications

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“…Currently, many hybridization approaches have been proposed to be the recent techniques that reduce the sparsity problem. The techniques create hybrid systems with multiple representations including demographic [15] and usergenerated content such as tags [16] [12] and social relationship information [17][16] [18] [19] to augment the accuracy of recommendation. However, when the user has limited historical data, these methods are still inadequate.…”

Section: Rs Approachesmentioning

confidence: 99%

Embedded Learning for Leveraging Multi-Aspect in Rating Prediction of Personalized Recommendation

Khairudin

Sharef

Noah

et al. 2018

MJCS

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Collaborative filtering that relies on overall ratings has been widely accepted due to the ability to generate satisfactory recommendations. However, the most challenging difficulty of this approach is the lack of sufficient ratings or the so-called data sparsity. Moreover, sometimes these ratings alone are not sufficient to precisely understand users' specific behaviours. A user may show his/her overall preferences on an item through the overall ratings but at the same time, they may not satisfy with certain aspects of the item. This situation happened due to the emphasis on aspects that may be different among users and will effect a user's final decisions. Therefore, in this paper, we proposed a model called Neural Network model for Multi-Aspect with Strong Correlation (NNMASC) that utilize the significance of aspect's correlation to enhance the predictive accuracy of personalized recommendation. We integrate the user, item, aspects and overall ratings via embedding features by utilizing the available multi-aspect ratings from hotel reviews dataset. NNMASC adopts a feed-forward neural network with back propagation algorithm to make rating prediction. The experimental result using MAE shows that the proposed model has significantly outperformed the traditional models and the state-of-the-art approaches in terms of prediction accuracy.

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Section: Rs Approachesmentioning

confidence: 99%

Embedded Learning for Leveraging Multi-Aspect in Rating Prediction of Personalized Recommendation

Khairudin

Sharef

Noah

et al. 2018

MJCS

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“…Quasit et al [29] proposed an implicit-trust based CF method, dubbed as hybrid user-item trust (HUIT), addressing the issues of data sparsity and cold start. The method combines predictions obtained using a user-based trust matrix with predictions obtained using an item-based trust matrix to make final predictions.…”

Section: Introductionmentioning

confidence: 99%

AgreeRelTrust—a Simple Implicit Trust Inference Model for Memory-Based Collaborative Filtering Recommendation Systems

2019

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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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“…One of the most popular algorithms for recommender systems is collaborative filtering (CF), which simply finds patterns among similar users or items [2]. CF achieved widespread success because of its simplicity and efficiency, despite several drawbacks (e.g., the sparsity problem) [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Improved Neighborhood Search for Collaborative Filtering

Chung¹,

Kim²,

Park³

et al. 2018

IJFIS

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k-Nearest Neighbor (k-NN) and other user-based collaborative filtering (CF) algorithms have gained popularity because of the simplicity of their algorithms and performance. As the performance of such algorithms largely depends on neighborhood selection, it is important to select the most suitable neighborhood for each active user. Previous user-based CF simply relies on similar users or common experts in this regard; however, because users have different tastes as well as different expectations for expert advice, similar users or common experts may not always be the best neighborhood for CF. In search of a more suitable neighborhood, so-called personalized experts develop personalized expert features. Through experimentation, we show that personalized experts are different from similar users, common experts, or similar common experts. The personalized, expert-based CF algorithm outperforms k-NN and other user-based CF algorithms.

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An effective recommender system by unifying user and item trust information for B2B applications

Cited by 60 publications

References 44 publications

Embedded Learning for Leveraging Multi-Aspect in Rating Prediction of Personalized Recommendation

Embedded Learning for Leveraging Multi-Aspect in Rating Prediction of Personalized Recommendation

AgreeRelTrust—a Simple Implicit Trust Inference Model for Memory-Based Collaborative Filtering Recommendation Systems

Improved Neighborhood Search for Collaborative Filtering

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