Reputation-Enhanced Recommender Systems

Richthammer, Christian; Weber, Michael; Pernul, Günther

doi:10.1007/978-3-319-59171-1_13

Cited by 4 publications

(5 citation statements)

References 41 publications

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“…The homophily (McPherson et al, 2001) and social influence (Marsden and Friedkin, 1993) theories associate social links to user similarity. On this basis, social and trust-based recommender systems (Richthammer et al, 2017) exploit social networks as additional sources of information to complement rating data. These systems estimate user preferences by relying on the known social links existing between people; e.g., friend, follower and/or trust relations according to different inference techniques; see • AVG: average rating of selected (e.g., trusted) social links Hendler, 2004, 2006;Liu and Lee, 2010;Parvin et al, 2019).…”

Section: Trust-based Recommender Systemsmentioning

confidence: 99%

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: Trust-based Recommender Systemsmentioning

confidence: 99%

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

Ardissono

Mauro

2020

Expert Systems with Applications

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“…Most trust-based recommenders leverage social influence to estimate ratings in Collaborative Filtering; see [41]. They assume that the trust relations between specific users can be inferred from social links; e.g., from friend associations and/or follower relations [5,14,15,19,28,29,33,43,45,46].…”

Section: Trust-based Recommender Systemsmentioning

confidence: 99%

Multi-faceted Trust-based Collaborative Filtering

Mauro¹,

Ardissono²,

Hu³

2019

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization

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Many collaborative recommender systems leverage social correlation theories to improve suggestion performance. However, they focus on explicit relations between users and they leave out other types of information that can contribute to determine users' global reputation; e.g., public recognition of reviewers' quality.We are interested in understanding if and when these additional types of feedback improve Top-N recommendation. For this purpose, we propose a multi-faceted trust model to integrate local trust, represented by social links, with various types of global trust evidence provided by social networks. We aim at identifying general classes of data in order to make our model applicable to different case studies. Then, we test the model by applying it to a variant of User-to-User Collaborative filtering (U2UCF) which supports the fusion of rating similarity, local trust derived from social relations, and multi-faceted reputation for rating prediction. We test our model on two datasets: the Yelp one publishes generic friend relations between users but provides different types of trust feedback, including user profile endorsements. The LibraryThing dataset offers fewer types of feedback but it provides more selective friend relations aimed at content sharing. The results of our experiments show that, on the Yelp dataset, our model outperforms both U2UCF and state-of-the-art trust-based recommenders that only use rating similarity and social relations. Differently, in the LibraryThing dataset, the combination of social relations and rating similarity achieves the best results. The lesson we learn is that multi-faceted trust can be a valuable type of information for recommendation. However, before using it in an application domain, an analysis of the type and amount of available trust evidence has to be done to assess its real impact on recommendation performance.

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Section: Related Workmentioning

confidence: 99%

“…According to Richthammer et al (2017) [20], reputation-enhanced recommender systems need further research. Richthammer et al [20] present an updated survey regarding reputation coupled with recommendation systems. Particularly, in the tourism domain, Bedi et al (2014) [2] propose a Multi-Agent Recommender System for e-Tourism (MARST) with item reputation modelling.…”

Section: Related Workmentioning

confidence: 99%

Trust and Reputation Modelling for Tourism Recommendations Supported by Crowdsourcing

Leal

Malheiro

Burguillo

2018

Advances in Intelligent Systems and Computing

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Abstract. Tourism crowdsourcing platforms have a profound influence on the tourist behaviour particularly in terms of travel planning. Not only they hold the opinions shared by other tourists concerning tourism resources, but, with the help of recommendation engines, are the pillar of personalised resource recommendation. However, since prospective tourists are unaware of the trustworthiness or reputation of crowd publishers, they are in fact taking a leap of faith when then rely on the crowd wisdom. In this paper, we argue that modelling publisher Trust & Reputation improves the quality of the tourism recommendations supported by crowdsourced information. Therefore, we present a tourism recommendation system which integrates: (i) user profiling using the multi-criteria ratings; (ii) k -Nearest Neighbours (k -NN) prediction of the user ratings; (iii) Trust & Reputation modelling; and (iv ) incremental model update, i.e., providing near real-time recommendations. In terms of contributions, this paper provides two different Trust & Reputation approaches: (i) general reputation employing the pairwise trust values using all users; and (ii) neighbour-based reputation employing the pairwise trust values of the common neighbours. The proposed method was experimented using crowdsourced datasets from Expedia and TripAdvisor platforms.

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Reputation-Enhanced Recommender Systems

Cited by 4 publications

References 41 publications

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

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

Multi-faceted Trust-based Collaborative Filtering

Trust and Reputation Modelling for Tourism Recommendations Supported by Crowdsourcing

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