Trust Metrics in Recommender Systems

Massa, Paolo; Avesani, Paolo

doi:10.1007/978-1-84800-356-9_10

Cited by 110 publications

(77 citation statements)

References 12 publications

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“…In [37], Massa and Avesani discuss some of the weaknesses of collaborative filtering systems. For instance, users typically rate or experience only a small fraction of the available items, which makes the rating matrix very sparse (since a recommender system often deals with millions of items).…”

Section: Motivationmentioning

confidence: 99%

“…Hence, unsurprisingly, some attempts in this direction have already been made, see for example [15,23,30,37,46,49,51]. Trust-enhanced recommender systems can roughly be divided into two classes, according to the way the trust values are obtained.…”

Section: State Of the Artmentioning

confidence: 99%

“…The first group uses information coming from a trust network that is generated by the direct input of the users, i.e., by explicitly issuing trust statements. Examples can be found in [16,23,37]. Such a strategy allows to use trust propagation and aggregation in the network to infer the final trust values that are needed in the recommender algorithm.…”

Section: State Of the Artmentioning

confidence: 99%

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Trust and Recommendations

Victor

Cock

Cornelis

2010

Recommender Systems Handbook

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Recommendation technologies and trust metrics constitute the two pillars of trust-enhanced recommender systems. We discuss and illustrate the basic trust concepts such as trust and distrust modeling, propagation and aggregation. These concepts are needed to fully grasp the rationale behind the trust-enhanced recommender techniques that are discussed in the central part of the chapter, which focuses on the application of trust metrics and their operators in recommender systems. We explain the benefits of using trust in recommender algorithms and give an overview of state-of-the-art approaches for trust-enhanced recommender systems. Furthermore, we explain the details of three well-known trust-based systems and provide a comparative analysis of their performance. We conclude with a discussion of some recent developments and open challenges, such as visualizing trust relationships in a recommender system, alleviating the cold start problem in a trust network of a recommender system, studying the effect of involving distrust in the recommendation process, and investigating the potential of other types of social relationships.

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Section: Motivationmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

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Trust and Recommendations

Victor

Cock

Cornelis

2010

Recommender Systems Handbook

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“…The recommender system is a subclass of information filtering system which can recommend services that a user would like. However, as the most popular recommendation mechanism, collaborative filtering (CF) suffers from the well-known data sparseness problem and the cold start problem [8][9][10][12][13]. The trust-aware recommender system (TARS) improves CF by suggesting the worthwhile information to the users on the basis of user trust: trust is the measure of willingness to believe in a user based on its competence and behavior within a specific context at a given time [8].…”

Section: Introductionmentioning

confidence: 99%

“…Most researches [12][13][14][15][16][21][22][23][24][25] did not provide any information how they find the recommenders. A few other works [8][9][10] briefly mentioned that they search the entire trust network to find the recommenders: since it does not miss any node reachable by the trust propagations, the TARS can achieve high prediction coverage.…”

Section: Introductionmentioning

confidence: 99%

Recommender Searching Mechanism for Trust-Aware Recommender Systems in Internet of Things

et al. 2013

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Original scientific paperIntelligent things are widely connected in Internet of Things (IoT) to enable ubiquitous service access. This may cause heavy service redundant. The trust-aware recommender system (TARS) is therefore proposed for IoT to help users finding reliable services. One fundamental requirement of TARS is to efficiently find as many recommenders as possible for the active users. To achieve this, existing approaches of TARS choose to search the entire trust network, which have very high computational cost. Though the trust network is the scale-free network, we show via experiments that TARS cannot find satisfactory number of recommenders by directly applying the classical searching mechanism. In this paper, we propose an efficient searching mechanism, named S_Searching: based on the scale-freeness of trust networks, choosing the global highest-degree nodes to construct a Skeleton, and searching the recommenders via this Skeleton. Benefiting from the superior outdegrees of the nodes in the Skeleton, S_Searching can find the recommenders very efficiently. Experimental results show that S_Searching can find almost the same number of recommenders as that of conducting full search, which is much more than that of applying the classical searching mechanism in the scale-free network, while the computational complexity and cost is much less.Key words: Searching Mechanism, Trust Network, Recommender System, Scale-freeness Mehanizam pretraživanja preporučitelja za sustave sigurnih preporučitelja u Internetu stvari. Inteligentni objekti su naširoko povezani u Internet stvari kako bi se omogućio sveprisutni pristup uslugama. To može imati za posljedicu veliku redundanciju usluga. Stoga je za pronalaženje pouzdane usluge u radu predložen vjerodostojan sustav preporučitelja (VSP). Temeljni zahtjev VSP-a je učinkovito pretraživanje maksimalnog mogućeg broja preporučtelja za aktivnog korisnika. Kako bi se to postiglo, postojeći pristupi VSP-a u potpunosti pretražuju sigurnu mrežu što ima za posljedicu velike računske zahtjeve. Iako je sigurna mreža mreža bez skale, eksperimentima je pokazano kako VSP ne može naći zadovoljavajući broj preporučitelja direktnom primjenom klasičnog algoritma pretraživanja. U ovom radu je predložen učinkovit algoritam pretraživanja, nazvan S_Searching: temeljen na sigurnim mrežama bez skale koji korističvorove globalno najvećeg stupnja za izgradnju Skeleton-a i pretražuje preporučitelja pomoću Skeleton-a. Iskorištavanjem nadreîenih izlaznih stupnjevačvorova Skeleton-a S_Searching može s visokom učinkovitošću pronaći preporučitelje. Eksperimentalni rezultati pokazuju kako S_Searching može naći gotovo jednak broj preporučitelja koji bi se pronašli potpunom pretragom, što je mnogo više od onoga što se postiže primjenom klasičnog algoritma pretrage na mreži bez skale, uz znatno smanjenje računske kompleksnosti i zahtjeva.Ključne riječi: algoritam pretraživanja, sigurna mreža, sustav preporučitelja, mreže bez skale

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A cloud service recommendation method based on extended multi‐source information fusion

Wang

Wen

Zhou

et al. 2022

Concurrency and Computation

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With the rapid development of information technology, the problem of "information overload" emerges when users choose cloud services. How to integrate multi-source information to achieve accurate service recommendation is an urgent problem to be solved by current recommendation systems. This article proposes a cloud service recommendation method based on extended multi-source information fusion. First, we propose a score prediction based on matrix decomposition and topic matrix, and we fully mine existing explicit data and feedback data, such as user ratings, social trust information, reviews, and user personalized preferences and so on. Second, in order to solve the problems of data sparseness and cold start of the system, we integrate the score, social trust information and review into a comprehensive model through collaborative filtering (CF), and propose a multi-source information fusion recommendation method. The CF fusion method mainly combines two parts: social matrix decomposition and topic matrix decomposition. Finally, in order to further improve the accuracy and scalability, the implicit feature matrix is integrated into the user rating matrix, and the original CF enhancement based on scoring matrix decomposition is a matrix decomposition method that can learn implicit features. Experimental results show that compared with other recommendation algorithms, the cloud service recommendation method proposed in this article can improve the recommendation accuracy and allow users to choose satisfactory cloud services.

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Trust Metrics in Recommender Systems

Cited by 110 publications

References 12 publications

Trust and Recommendations

Trust and Recommendations

Recommender Searching Mechanism for Trust-Aware Recommender Systems in Internet of Things

A cloud service recommendation method based on extended multi‐source information fusion

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