Trinity: Walking on a User-Object-Tag Heterogeneous Network for Personalised Recommendations

Gan, Mingxin; Sun, Lily; Jiang, Rui

doi:10.1007/s11390-016-1648-0

Cited by 7 publications

(5 citation statements)

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“…Also, other traditional approaches exist that can be used to improve CF and include the use of content-boosted CF or the utilization of sparsity measures (Anand & Bharadwaj, 2011;Melville, Mooney, & Nagarajan, 2002). COUSIN is a recommendation model that improves both the accuracy and the diversity of the recommendations by using a regression model that effectively removes weak user relationships (M. Gan, 2016). There is also an approach in the literature called Trinity that uses historical data and tags to provide personalized recommendations based on a three-layered object-user tag network (M.-X.…”

Section: Collaborative Filteringmentioning

confidence: 99%

“…There is also an approach in the literature called Trinity that uses historical data and tags to provide personalized recommendations based on a three-layered object-user tag network (M.-X. Gan, Sun, & Jiang, 2016). In addition to the methods mentioned already the use of user-item subgroups has been proposed as a way of providing improved recommendation systems (Xu, Bu, Chen, & Cai, 2012).…”

Section: Collaborative Filteringmentioning

confidence: 99%

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From product recommendation to cyber-attack prediction: generating attack graphs and predicting future attacks

et al. 2018

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Modern information society depends on reliable functionality of information systems infrastructure, while at the same time the number of cyber-attacks has been increasing over the years and damages have been caused. Furthermore, graphs can be used to show paths than can be exploited by attackers to intrude into systems and gain unauthorized access through vulnerability exploitation. This paper presents a method that builds attack graphs using data supplied from the maritime supply chain infrastructure. The method delivers all possible paths that can be exploited to gain access. Then, a recommendation system is utilized to make predictions about future attack steps within the network. We show that recommender systems can be used in cyber defense by predicting attacks. The goal of this paper is to identify attack paths and show how a recommendation method can be used to classify future cyber-attacks in terms of risk management. The proposed method has been experimentally evaluated and validated, with the results showing that it is both practical and effective.

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Section: Collaborative Filteringmentioning

confidence: 99%

Section: Collaborative Filteringmentioning

confidence: 99%

From product recommendation to cyber-attack prediction: generating attack graphs and predicting future attacks

et al. 2018

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“…COUSIN is a recommendation model that improves both the accuracy and the diversity of the recommendations by using a regression model that affectively removes weak user relationships [16]. There is also an approach in the literature called Trinity that uses historical data and tags to provide personalized recommendations based on a threelayered object-user tag network [17]. In addition to the methods mentioned already the use of user-item subgroups has been proposed as a way of providing improved recommendation systems [18].…”

Section: Collaborative Filteringmentioning

confidence: 99%

Recommender Systems Meeting Security: From Product Recommendation to Cyber-Attack Prediction

Polatidis

Pimenidis

Pavlidis

et al. 2017

Engineering Applications of Neural Networks

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Abstract. Modern information society depends on reliable functionality of information systems infrastructure, while at the same time the number of cyberattacks has been increasing over the years and damages have been caused. Furthermore, graphs can be used to show paths than can be exploited by attackers to intrude into systems and gain unauthorized access through vulnerability exploitation. This paper presents a method that builds attack graphs using data supplied from the maritime supply chain infrastructure. The method delivers all possible paths that can be exploited to gain access. Then, a recommendation system is utilized to make predictions about future attack steps within the network. We show that recommender systems can be used in cyber defense by predicting attacks. The goal of this paper is to identify attack paths and show how a recommendation method can be used to classify future cyber-attacks. The proposed method has been experimentally evaluated and it is shown that it is both practical and effective.

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“…Shang et al [13] proposed an user-based hybrid tag algorithm by harnessing diffusion-based method(UDiff). Trinity is introduced in [14], where a random walk with restart model is proposed based on a three-layered object-user-tag heterogeneous network.…”

Section: Related Workmentioning

confidence: 99%

“…• PMF [14]: It models user-item rating matrix by matrix factorization, including adaptive priors over the item and user feature vectors that can be used to control model complexity automatically.…”

Section: Comparisonsmentioning

confidence: 99%

Leveraging tagging and rating for recommendation: RMF meets weighted diffusion on tripartite graphs

Tang

Chen

2017

Physica A: Statistical Mechanics and its Applications

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Recommender systems (RSs) have been a widely exploited approach to solving the information overload problem. However, the performance is still limited due to the extreme sparsity of the rating data. With the popularity of Web 2.0, the social tagging system provides more external information to improve recommendation accuracy. Although some existing approaches combine the matrix factorization models with co-occurrence properties and context of tags, they neglect the issue of tag sparsity without the commonly associated tags problem that would also result in inaccurate recommendations. Consequently, in this paper, we propose a novel hybrid collaborative filtering model named WUDiff RMF, which improves Regularized Matrix Factorization (RMF) model by integrating Weighted User-Diffusion-based CF algorithm(WUDiff) that obtains the information of similar users from the weighted tripartite user-item-tag graph. This model aims to capture the degree correlation of the user-item-tag tripartite network to enhance the performance of recommendation. Experiments conducted on four real-world datasets demonstrate that our approach significantly performs better than already widely used methods in the accuracy of recommendation. Moreover, results show that WUDiff RMF can alleviate the data sparsity, especially in the circumstance that users have made few ratings and few tags.

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Trinity: Walking on a User-Object-Tag Heterogeneous Network for Personalised Recommendations

Cited by 7 publications

References 50 publications

From product recommendation to cyber-attack prediction: generating attack graphs and predicting future attacks

From product recommendation to cyber-attack prediction: generating attack graphs and predicting future attacks

Recommender Systems Meeting Security: From Product Recommendation to Cyber-Attack Prediction

Leveraging tagging and rating for recommendation: RMF meets weighted diffusion on tripartite graphs

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