Exploiting Trust and Usage Context for Cross-Domain Recommendation

Xu, Zhenzhen; Zhang, Fuli; Wang, Wei; Liu, Haifeng; Kong, Xiangjie

doi:10.1109/access.2016.2566658

Cited by 21 publications

(9 citation statements)

References 31 publications

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“…Xu et al proposed a method for solving a cold start problem in cross-domain recommendation scenario [71]. The method predicted the rating of items for source users via target domain items using trust relations.…”

Section: B Cross Domain Recommender Systemsmentioning

confidence: 99%

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Modeling Trust-Aware Recommendations With Temporal Dynamics in Social Networks

et al. 2020

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In recent years, user's trust has gained attention in recommender systems. Trust plays a vital role in the recommendation of online products. Trust is a dynamic feature which evolves with passage of time and varies from person to person. Trust-based cross domain recommender systems suggest items to the users usually by ratings, provided by similar users, often not available in the same domain. However, due to the sparse rating scores, recommender systems cannot generate up-to-the-mark recommendations. In this research, we solved a user cold start problem, mainly by modeling preference drift on a temporal basis. We tried to solve this problem by adopting one of the scenarios of cross domain of 'No Overlap' using cross domain information. In this work, we proposed a model called Trust Aware Cross Domain Temporal Recommendations (TrustCTR) that predict the rating of an item about an active user from the most recent time. We generated user features and item features by using latent factor model and trained the proposed model. We also introduced the concept of trust relevancy that shows the degree of trust, computed the trusted neighbors in target domain for an active user belonging to a source domain, and predicted the ratings of items for cold start users. We performed experiments on public datasets Ciao and Epinions and used these datasets in cross domain form such as the categories of Ciao as source domain and Epinions as the target domain. We selected five different domains, having a higher proportion of rating sparsity, for observing the performance of our approach using MAE, RMSE, and F-measure. Our approach is a viable solution of cold start problem and offers effective recommendations We also compared the model with state-of-the-art methods; the model generates satisfactory results.

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Section: B Cross Domain Recommender Systemsmentioning

confidence: 99%

“…For evaluation, we used Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and F-measure. The RMSE metric shows the closeness of predictions to outcomes [71], as described in equation (35). RMSE can be computed as…”

Section: A Evaluation Metricsmentioning

confidence: 99%

Modeling Trust-Aware Recommendations With Temporal Dynamics in Social Networks

et al. 2020

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“…To alleviate the problem of data sparsity, several methods have been proposed to apply some auxiliary information to the recommendation process. In addition to considering the ratings information [35]- [37], some studies added information about social networks to address the issue of data sparsity. For example, Shi et al [36] proposes a flexible regularization framework that integrates different types of users and items relationship information into the referral process.…”

Section: Related Workmentioning

confidence: 99%

“…For example, Shi et al [36] proposes a flexible regularization framework that integrates different types of users and items relationship information into the referral process. Xu et al [37] uses the trust relationship between users to improve the performance of the recommendation system.…”

Section: Related Workmentioning

confidence: 99%

Cold Start Recommendation Based on Attribute-Fused Singular Value Decomposition

et al. 2019

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Collaborative filtering plays an important role in promoting the service recommendation ecosystem, and the matrix decomposition technology has been proven to be one of the most effective recommendation methods. However, the traditional collaborative filtering algorithm has great shortcomings in the recommendation of cold start items, especially the emergence of new items will be largely ignored. This not only has a very bad impact on the development of the item, but also greatly reduces the diversity of the recommendation system. The rise of mobile devices has also brought a large number of mobile applications, and these emerging applications need to be promoted in order to maintain the robustness of the application system. In order to solve this problem, we propose a method of combining the attribute information of the item with the historical rating matrix to predict the potential preferences of the user. It combines the attribute and time information into a matrix decomposition model. By testing our method on the movielens and the climbed JD dataset, the experimental results show that, compared with the baseline method, the proposed method achieves a significant improvement in recommendation accuracy. Therefore, this method is an effective way to solve the cold start problem of new items.

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“…However, domains have different security goals that result in heterogeneous access control mechanisms and access policies [10,11]. erefore, a number of cross-domain access control mechanisms have been proposed to realize secure resource sharing between heterogeneous domains [12][13][14][15][16], but almost all of them are based on a single-server architecture. Existing cross-domain access control mechanisms based on single-server architectures generally have the following limitations:…”

Section: Introductionmentioning

confidence: 99%

Trusted and Efficient Cross-Domain Access Control System Based on Blockchain

Sun

Chen

2020

Scientific Programming

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In a distributed system, cross-domain access control is an important mechanism to realize secure data sharing among multiple domains. Most of the existing cross-domain access control mechanisms are generally based on a single-server architecture, which has limitations in terms of security and reliability (the access decision may be incorrect) and completeness and confidentiality (the access records can be modified). Blockchain technology with decentralization, verifiability, and immutability properties can solve these problems. Motivated by these facts, in this article, we construct a trusted and efficient cross-domain access control system based on blockchain. Consequently, we integrate blockchain and role mapping technology to provide reliable and verifiable cross-domain access process. We use blockchain to record user roles, role mapping rules, access policies, and audit records, realizing user self-validation, and access nonreputation. Considering the low throughput of the blockchain, we design an efficient smart contract to make the access decision based on the access history of users. Finally, a performance evaluation of the system is presented to demonstrate the feasibility of the proposed system.

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Exploiting Trust and Usage Context for Cross-Domain Recommendation

Cited by 21 publications

References 31 publications

Modeling Trust-Aware Recommendations With Temporal Dynamics in Social Networks

Modeling Trust-Aware Recommendations With Temporal Dynamics in Social Networks

Cold Start Recommendation Based on Attribute-Fused Singular Value Decomposition

Trusted and Efficient Cross-Domain Access Control System Based on Blockchain

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