Recommender systems

Yan, Wei; Moreau, Luc; Jennings, Nicholas R.

doi:10.1145/860575.860671

Cited by 26 publications

(1 citation statement)

References 12 publications

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“…Information aggregators and social media platforms have deployed content filters that censor non-credible and potentially deceptive claims (Aldwairi and Alwahedi 2018;Kumar and Geethakumari 2014). Recommender systems learn consumers' preferences to save them from having to sift through unwanted content (Bagher, Hassanpour, and Mashayekhi 2017;Wei, Moreau, and Jennings 2003;Bergemann and Ozmen 2006). 1 Despite major efforts to improve content filters, information consumers remain susceptible to malicious or illegitimate content, e.g., they click on phishing messages (Blythe, Petrie, and Clark 2011;Benenson, Gassmann, and Landwirth 2017) and fall victim to misinformation (Roozenbeek et al 2020;Pennycook and Rand 2019).…”

Section: Introductionmentioning

confidence: 99%

Content Filtering with Inattentive Information Consumers

Ball,

Bono,

Grana

et al. 2024

AAAI

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We develop a model of content filtering as a game between the filter and the content consumer, where the latter incurs information costs for examining the content. Motivating examples include censoring misinformation, spam/phish filtering, and recommender systems acting on a stream of content. When the attacker is exogenous, we show that improving the filter’s quality is weakly Pareto improving, but has no impact on equilibrium payoffs until the filter becomes sufficiently accurate. Further, if the filter does not internalize the consumer’s information costs, its lack of commitment power may render it useless and lead to inefficient outcomes. When the attacker is also strategic, improvements in filter quality may decrease equilibrium payoffs.

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Section: Introductionmentioning

confidence: 99%

Content Filtering with Inattentive Information Consumers

Ball,

Bono,

Grana

et al. 2024

AAAI

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Market-Based Recommender Systems: Learning Users’ Interests by Quality Classification

Yan

Moreau

Jennings

2005

Agent-Oriented Information Systems II

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Recommender systems are widely used to cope with the problem of information overload and, consequently, many recommendation methods have been developed. However, no one technique is best for all users in all situations. To combat this, we have previously developed a market-based recommender system that allows multiple agents (each representing a different recommendation method or system) to compete with one another to present their best recommendations to the user. In our system, the marketplace encourages good recommendations by rewarding the corresponding agents according to the users' ratings of their suggestions. Moreover, we have shown this incentivises the agents to bid in a manner that ensures only the best recommendations are presented. To do this effectively, however, each agent needs to classify its recommendations into different internal quality levels, learn the users' interests and adapt its bidding behaviour for the various internal quality levels accordingly. To this end, in this paper, we develop a reinforcement learning and Boltzmann exploration strategy that the recommending agents can exploit for these tasks. We then demonstrate that this strategy helps the agents to effectively obtain information about the users' interests which, in turn, speeds up the market convergence and enables the system to rapidly highlight the best recommendations.

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A Protocol for a Distributed Recommender System

Vidal

2005

Trusting Agents for Trusting Electronic Societies

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Abstract. We present a domain model and protocol for the exchange of recommendations by selfish agents without the aid of any centralized control. Our model captures a subset of the realities of recommendation exchanges in the Internet. We provide an algorithm that selfish agents can use for deciding whether to exchange recommendations and with whom. We analyze this algorithm and show that, under certain common circumstances, the agents' rational choice is to exchange recommendations. Finally, we have implemented our model and algorithm and tested the performance of various populations. Our results show that both the social welfare and the individual utility of the agents is increased by participating in the exchange of recommendations.

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Recommender systems

Cited by 26 publications

References 12 publications

Content Filtering with Inattentive Information Consumers

Content Filtering with Inattentive Information Consumers

Market-Based Recommender Systems: Learning Users’ Interests by Quality Classification

A Protocol for a Distributed Recommender System

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