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

Zahir, Ahmed; Yuan, Yunhao; Moniz, Krishna

doi:10.3390/electronics8040427

Cited by 18 publications

(17 citation statements)

References 30 publications

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“…On the other side, in item-item TBRS, the reliance of items is measured by applying users' feedback on the items [56] or studying users' activity with these items [28,57,58].However, according to the methodology of trust integration, TBRS can be categorized as memory-based [30,38,59] and model-based [20,38,60,61] approaches. Further, the TBRS can be classified based on the trust definition as either explicit [19,38,52,62] or implicit [12,13,17,18] or hybrid trust-based recommender system [35,63,64]. Figure 3 shows the classification of TBRS in a row.…”

Section: Classification Of Trust-based Recommender Systemmentioning

confidence: 99%

“…ITM11 : Zahir et al [12] also applied the liked and disliked items concept in their trust metric and calculated the trust, named as AgreeRelTrust, by combining users' agreements and relative activities in the system. The agreement A u,v of a pair of user is defined according to the positive and negative agreements in co-rated items where positive agreement denotes liked items by both users, and negative agreement determines the disliked items of both users.…”

Section: Implicit Trust-based Recommender Systemmentioning

confidence: 99%

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A Comparative Analysis of Different Trust Metrics in User-User Trust-Based Recommender System

F¹

2020

Preprint

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Information overload is the biggest challenges nowadays for any website, especially the e-commerce website. 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 like 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 aim of the paper is to present a comparative analysis of different trust metrics in the context of the type of trust definition of TBRS. Also, the study accomplishes on twenty-four trust metrics in terms of the methodology, trust properties &amp; measurement, validation approaches, and the experimented dataset.

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Section: Classification Of Trust-based Recommender Systemmentioning

confidence: 99%

Section: Implicit Trust-based Recommender Systemmentioning

confidence: 99%

A Comparative Analysis of Different Trust Metrics in User-User Trust-Based Recommender System

F¹

2020

Preprint

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“…The current recommendation system requires further enhancements to make the referral method more effective and broader [23]. You can apply it to a wider range of real-world applications, including vacation, investor-specific types of financial services, and products recommended for purchase in stores [24]. These enhancements include better ways to represent user behavior and proposed information, as well as advanced recommendation modeling a methodology that incorporates various contextual information into the referral process, uses multi-criteria assessments and develops a more intrusive and flexible referral method that is more effective based on the performance of the referral system [25,26].…”

Section: Literature Reviewmentioning

confidence: 99%

Design and Development of a Real-Time Optimal Route Recommendation System Using Big Data for Tourists in Jeju Island

2019

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Nowadays researchers and engineers are trying to build travel route recommendation systems to guide tourists around the globe. The tourism industry is on the rise and it has attracted researchers to provide such systems for comfortable and convenient traveling. Mobile internet growth is increasing rapidly. Mobile data usage and traffic growth has increased interest in building mobile applications for tourists. This research paper aims to provide design and implementation of a travel route recommendation system based on user preference. Real-time big data is collected from Wi-Fi routers installed at more than 149 unique locations in Jeju Island, South Korea. This dataset includes tourist movement patterns collected from thousands of mobile tourists in the year 2016–2017. Data collection and analysis is necessary for a country to make public policies and development of the global travel and tourism industry. In this research paper we propose an optimal travel route recommendation system by performing statistical analysis of tourist movement patterns. Route recommendation is based on user preferences. User preference can vary over time and differ from one user to another. We have taken three main factors into consideration to the recommend optimal route i.e., time, distance, and popularity of location. Beside these factors, we have also considered weather and traffic condition using a third-party application program interfaces (APIs). We have classified regions into six major categories. Popularity of location can vary from season to season. We used a Naïve Bayes classifier to find the probability of tourists going to visit next location. Third-party APIs are used to find the longitude and latitude of the location. The Haversine formula is used to calculate the distance between unique locations. On the basis of these factors, we recommend the optimal route for tourists. The proposed system is highly responsive to mobile users. The results of this system show that the recommended route is convenient and allows tourists to visit maximum number of famous locations as compared to previous data.

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“…However, this model has considerably higher computation costs because of the initial prediction calculation during the trust matrix generation. Addressing these issues with prediction accuracy-based trust models, Zahir et al [13] proposed AgreeRelTrust, which is a model that uses the agreement between users to infer trust, eliminating the need to calculate the initial predictions. This method also takes into account the activeness of the users when calculating the trust; however, the method is a memory-based model, and is thus not as computationally efficient as the MF variants.…”

Section: Related Workmentioning

confidence: 99%

“…ImpFactor = sqr(len(I u )) × sum(I u ) 13: Since both the trust and latent features are estimated together, they are trained with the same number of epochs. Then, the outputs of Algorithm 1 are combined using any of the three combination methods mentioned earlier.…”

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confidence: 99%

Modeling Implicit Trust in Matrix Factorization-Based Collaborative Filtering

2019

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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). Appl. Sci. 2019, 9, 4378 2 of 18 regularization factor is used to prevent overfitting. The singular value decomposition (SVD) is imposed as a baseline for model-based CF. However, MF models are difficult to generalize to the data when the original rating matrix is sparse. Several approaches have been proposed to address this challenge.The simplest and most common approach to resolve the sparsity problem involves inputting artificial data into the original matrix, and mean ratings are often used for this purpose. To address the problems associated with data sparsity in low-rank matrix approximation, Sebrero and Jaakkola [7] proposed a model that uses a weight inversely proportional to the noise variance. Similarly, Lee et al. [8] proposed an approach, namely, local low-rank matrix approximation (LLORMA), which approximates the observed matrix as the weighted sum of local low-rank matrices that are targeted to the local regions of the observed matrix. To construct the final approximated matrix, several local models are aggregated. Mackey et al. [9] proposed a similar method referred to as divide-and-conquer (DFC) MF. In contrast to LLORMA, DFC uses overlapping partitions of the observed matrix to construct the local models.To improve the efficiency of recommendation systems, the use of side information has been widely researched. Apart from side information such as the context and user and item characteristics, trust is often used as a reliable measure to incorporate into the CF technique. However, obtaining the explicit trust link between users is difficult. Here, we propose a new trust measure that can be derived directly from the user's preference matrix. In contrast to most trust metrics, which measure the degree of reliability between two users, our proposed metrics measure the user's trust regarding the preference they have expressed. T...

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

Cited by 18 publications

References 30 publications

A Comparative Analysis of Different Trust Metrics in User-User Trust-Based Recommender System

A Comparative Analysis of Different Trust Metrics in User-User Trust-Based Recommender System

Design and Development of a Real-Time Optimal Route Recommendation System Using Big Data for Tourists in Jeju Island

Modeling Implicit Trust in Matrix Factorization-Based Collaborative Filtering

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