Personalized Recommendation via Trust-Based Diffusion

Liu, Yuanzhen; Han, Lixin; Gou, Zhinan; Yang, Yi

doi:10.1109/access.2019.2928574

Cited by 5 publications

(4 citation statements)

References 40 publications

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“…Logesh et al [12] propose a social pertinent trust walker model, which utilizes a modified random walk based on trust pertinence calculated by matrix factorization for POI recommendation. Liu et al [29] propose a trust-aware recommendation model, where the trust relationship is measured by combining explicit and implicit trust between users. Ahmadian et al [30] propose an effective neighbor selection mechanism for removing unreliable users from the nearest neighbor set, which enhance the accuracy of recommendations.…”

Section: Trust-enhanced Poi Recommendationsmentioning

confidence: 99%

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Bidirectional Trust-Enhanced Collaborative Filtering for Point-of-Interest Recommendation

Jiang

2023

Sensors

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A personalized point-of-interest (POI) recommender system is of great significance to facilitate the daily life of users. However, it suffers from some challenges, such as trustworthiness and data sparsity problems. Existing models only consider the trust user influence and ignore the role of the trust location. Furthermore, they fail to refine the influence of context factors and fusion between the user preference and context models. To address the trustworthiness problem, we propose a novel bidirectional trust-enhanced collaborative filtering model, which investigates the trust filtering from the views of users and locations. To tackle the data sparsity problem, we introduce temporal factor into the trust filtering of users as well as geographical and textual content factors into the trust filtering of locations. To further alleviate the sparsity of user-POI rating matrices, we employ a weighted matrix factorization fused with the POI category factor to learn the user preference. To integrate the trust filtering models and the user preference model, we develop a fused framework with two kinds of integrating methods in relation to the different impacts of factors on the POIs that users have visited and the POIs that users have not visited. Finally, we conduct extensive experiments on Gowalla and Foursquare datasets to evaluate our proposed POI recommendation model, and the results show that our proposed model improves by 13.87% at precision@5 and 10.36% at recall@5 over the state-of-the-art model, which demonstrates that our proposed model outperforms the state-of-the-art method.

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Section: Trust-enhanced Poi Recommendationsmentioning

confidence: 99%

“…min (29) where we introduce a regularization term ∑ |C| c=1 ||v c || 2 for the POI category, and λ v is the corresponding regularization coefficient. Furthermore, we observe that if u i has visited l j (C i,j = 1), then I i,j + H i,j will fit to C i,j = 1.…”

Section: Fused Modelmentioning

confidence: 99%

Bidirectional Trust-Enhanced Collaborative Filtering for Point-of-Interest Recommendation

Jiang

2023

Sensors

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“…In order to verify the validity and superiority of DWMD proposed, our algorithm is compared with four benchmark methods on the Epinions and Cao data sets, which is the item-based collaborative filtering recommendation model (Item-based CF), mass diffusion (MD) [7], CosRA + T model, and trust-based mass diffusion model (TrustMD) [29]. In the Item-based CF model, it is recommended that the objects are similar to those collected by the target user, and the cosine similarity is used to calculate the object similarity.…”

Section: Recommendation Performancementioning

confidence: 99%

Recommendation with Diversity: Mass Diffusion Model Based on Trust Network and Object Reputation

Bai

Cai

2021

Mobile Information Systems

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The traditional mass diffusion recommendation algorithm only relies on the user’s object collection relationship, resulting in poor recommendation performance for users with small purchases (i.e., small-degree user), and it is difficult to balance the accuracy and diversity of the recommendation system. This paper introduces the trust relationship into the resource allocation process of the traditional mass diffusion algorithm and proposes the Dual Wing Mass Diffusion model (DWMD), which constructs a dual wing graph based on trust relationships and object collection relationships. Implicit trust is mined according to the network structure of the trust relationship and integrated into the resource allocation process, and then merging the positive effects of object reputation on a recommendation through tunable scaling parameters. The user controls the tunable scaling parameter to achieve the best recommendation performance. The experimental results show that the DWMD method significantly improves diversity and novelty while ensuring high accuracy and effectively improves the accuracy and diversity balance. The improved recommendation performance for small-degree users proves that the trust relationship can effectively alleviate the generalized cold start problem of the recommendation algorithm for users who collect a small number of objects.

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“…They are not only easy to implement and very efficient but can also produce relatively stable and accurate recommendation results. In addition to item-oriented and user-oriented CF algorithms, some researchers also incorporate these two algorithms into the similarity fusion framework [26], [27].…”

Section: B Neighborhood-based Modelmentioning

confidence: 99%

A Novel Learning Model Based on Trust Diffusion and Global Item for Recommender Systems

Liu

Ren

2019

IEEE Access

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Recommender systems can provide users with an ordered list of various items, which greatly assists users to purchase products that they are satisfied with. However, item recommendation has been confronted with some inherent problems, such as sparse ratings and long-tail distribution, resulting in low accuracy of recommendations and insignificant marketing. In this paper, we propose a novel learning model based on trust diffusion and global item (TDGIL) to improve the accuracy of item rating prediction for recommender systems. Specifically, first, the rating information on items is mined and aggregated to the greatest extent based on trust diffusion characteristics among users. The benchmark prediction of item recommendation is updated by a user trust neighbor set and its item ratings, which are obtained by a trust diffusion algorithm. Then, the difference weights and compensation coefficients for all items are defined to learn users' potential preferences in the proposed global item model. Finally, the TDGIL learning algorithm is presented to train and learn the target networks by random gradient descent. The extensive experiments and results on two real-world datasets demonstrated that our proposed model can achieve significant improvements in the accuracy of rating prediction compared with some state-of-the-art methods.

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Personalized Recommendation via Trust-Based Diffusion

Cited by 5 publications

References 40 publications

Bidirectional Trust-Enhanced Collaborative Filtering for Point-of-Interest Recommendation

Bidirectional Trust-Enhanced Collaborative Filtering for Point-of-Interest Recommendation

Recommendation with Diversity: Mass Diffusion Model Based on Trust Network and Object Reputation

A Novel Learning Model Based on Trust Diffusion and Global Item for Recommender Systems

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