TCFACO: Trust-aware collaborative filtering method based on ant colony optimization

Parvin, Hashem; Moradi, Parham; Esmaeili, Shahrokh

doi:10.1016/j.eswa.2018.09.045

Cited by 73 publications

(32 citation statements)

References 35 publications

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“…Algorithm Technology User filtering Social regularization Reputation (Golbeck and Hendler, 2006) FilmTrust AVG by trust -- (Kuter and Golbeck, 2007 • AVG: average rating of selected (e.g., trusted) social links Hendler, 2004, 2006;Liu and Lee, 2010;Parvin et al, 2019).…”

Section: Citationmentioning

confidence: 99%

“…Finally, SocialMF (Jamali and Ester, 2010) employs rating similarity to regularize the impact of users who are reachable through a short path of social links in Random Walk. Other systems achieve similar filtering results by combining trust-based and item-based recommendation, as in TrustWalker (Jamali and Ester, 2009), or by filtering the users of the trust networks according to rating similarity, as in TCFACO (Parvin et al, 2019) and RelevantTrustWalker (Deng et al, 2014). Finally, Du et al (2017) apply co-clustering to the matrices of ratings and social relations in order to identify like-minded users within social connections.…”

Section: Citationmentioning

confidence: 99%

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A compositional model of multi-faceted trust for personalized item recommendation

Ardissono

Mauro

2020

Expert Systems with Applications

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We propose a compositional recommender system based on multi-faceted trust.• The trust model is based on social links and global feedback about users.• The recommender can work with or without using information about social relations.• We validate our recommendation model on two public datasets of item reviews.• Our recommender outperforms state-of-the-art trust-based recommender systems. AbstractTrust-based recommender systems improve rating prediction with respect to Collaborative Filtering by leveraging the additional information provided by a trust network among users to deal with the cold start problem. However, they are challenged by recent studies according to which people generally perceive the usage of data about social relations as a violation of their own privacy. In order to address this issue, we extend trust-based recommender systems with additional evidence about trust, based on public anonymous information, and we make them configurable with respect to the data that can be used in the given application domain:1. We propose the Multi-faceted Trust Model (MTM) to define trust among users in a compositional way, possibly including or excluding the types of information it contains. MTM flexibly integrates social links with public anonymous feedback received by user profiles and user contributions in social networks.2. We propose LOCABAL+, based on MTM, which extends the LOCA-BAL trust-based recommender system with multi-faceted trust and trust-based social regularization.Experiments carried out on two public datasets of item reviews show that, with a minor loss of user coverage, LOCABAL+ outperforms state-of-the art trust-based recommender systems and Collaborative Filtering in accuracy, ranking of items and error minimization both when it uses complete information about trust and when it ignores social relations. The combination of MTM with LOCABAL+ thus represents a promising alternative to state-of-the-art trust-based recommender systems.

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Section: Citationmentioning

confidence: 99%

Section: Citationmentioning

confidence: 99%

A compositional model of multi-faceted trust for personalized item recommendation

Ardissono

Mauro

2020

Expert Systems with Applications

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“…Although large numbers of users and items generally participate in an RS, user feedback on items is usually limited. A simple and effective way to improve RS accuracy is to consider information sources such as demographic characteristics [29] or information about friends of users in social networks [30][31][32][33]. Before making decisions, real-world users tend to consider the opinions and suggestions of friends they trust.…”

Section: Social-based Recommender Systemsmentioning

confidence: 99%

“…Another approach used in [32] was to explicitly merge the performance of trusted neighbors and the active user by averaging their ratings, which eliminated the data sparsity problem. In [33], an ant colony optimization algorithm was used in a trust graph to identify a set of users that are like the active user. In [19], a combination of similarity values and trust statements was used to construct a trust network for the active user.…”

Section: Social-based Recommender Systemsmentioning

confidence: 99%

Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems

et al. 2020

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Conventional recommender systems are designed to achieve high prediction accuracy by recommending items expected to be the most relevant and interesting to users. Therefore, they tend to recommend only the most popular items. Studies agree that diversity of recommendations is as important as accuracy because it improves the customer experience by reducing monotony. However, increasing diversity reduces accuracy. Thus, a recommendation algorithm is needed to recommend less popular items while maintaining acceptable accuracy. This work proposes a two-stage collaborative filtering optimization mechanism that obtains a complete and diversified item list. The first stage of the model incorporates multiple interests to optimize neighbor selection. In addition to using conventional collaborative filtering to predict ratings by exploiting available ratings, the proposed model further considers the social relationships of the user. A novel ranking strategy is then used to rearrange the list of top-N items while maintaining accuracy by (1) rearranging the area controlled by the threshold and by (2) maximizing popularity while maintaining an acceptable reduction in accuracy. An extensive experimental evaluation performed in a real-world dataset confirmed that, for a given loss of accuracy, the proposed model achieves higher diversity compared to conventional approaches.

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“…There are two ways to combine recommendation algorithm with ant colony algorithm. One is to use ant colony algorithm to optimize the recommendation algorithm [31], [32], and the other is to use the recommendation algorithm to optimize the ant colony algorithm [33]. Experiments of both methods verify the effectiveness of the algorithm and optimize the overall performance of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Multi-Colony Ant Colony Optimization Based on Generalized Jaccard Similarity Recommendation Strategy

et al. 2019

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Ant Colony Optimization has achieved good results in solving Traveling Salesman Problem (TSP), it has a tendency to fall into local optima and the convergence speed is limited. To address this problem, multi-colony ant colony optimization based on the generalized Jaccard similarity recommendation strategy (JCACO) is proposed. Firstly, two classical ant populations, Ant Colony System and Max-Min Ant System are selected to form heterogeneous multi-colony. Secondly, attribute-based collaborative filtering recommendation mechanism is proposed to balance the diversity and convergence of the algorithm, three strategies have been implemented under this recommendation mechanism: The attribute cross-learning strategy is used to highlight the effect of excellent attributes and improve the attribute comprehensive performance; According to the diversity results of the population measured by information entropy, the attribute recommendation learning strategy is used to enrich the diversity of the population adaptively; The pheromone reward strategy is implemented on the public path to accelerate the convergence speed; Among which, according to the generalized Jaccard similarity coefficient, the most suitable communication object is recommended in order to achieve the best learning efficiency. Finally, when the algorithm stagnates, the elite reverse learning mechanism is used to jump out of the local optimum. Experimental results show that JCACO has good performance and high stability in TSP instances, especially in large-scale TSP instances. INDEX TERMS Attribute cross-learning, attribute recommendation learning, ant colony optimization, elite reverse learning, generalized Jaccard similarity coefficient, traveling salesman problem.

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TCFACO: Trust-aware collaborative filtering method based on ant colony optimization

Cited by 73 publications

References 35 publications

A compositional model of multi-faceted trust for personalized item recommendation

A compositional model of multi-faceted trust for personalized item recommendation

Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems

Multi-Colony Ant Colony Optimization Based on Generalized Jaccard Similarity Recommendation Strategy

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