The Limits of Popularity-Based Recommendations, and the Role of Social Ties

Bressan, Marco; Leucci, Stefano; Panconesi, Alessandro; Raghavan, Prabhakar; Terolli, Erisa

doi:10.1145/2939672.2939797

Cited by 22 publications

(22 citation statements)

References 35 publications

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“…For instance, they model the expenditure pattern of targeted consumers to predict the effect of a business strategy, such as a recommendation system, on actual consumption [27]. Models of consumer behavior in marketing studies incorporate various factors, including the structure of consumers' network [28,29], selfrevealed information in social media [30,31], and spatial information regarding the consumer's geographical location [32]. Among many factors that could explain the observed consumption patterns, the sequence of temporal actions has been particularly studied to understand consumers dynamic behavior [33][34][35][36][37].…”

Section: Related Workmentioning

confidence: 99%

Detecting multi-timescale consumption patterns from receipt data: a non-negative tensor factorization approach

et al. 2020

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Understanding consumer behavior is an important task, not only for developing marketing strategies but also for the management of economic policies. Detecting consumption patterns, however, is a high-dimensional problem in which various factors that would affect consumers' behavior need to be considered, such as consumers' demographics, circadian rhythm, seasonal cycles, etc. Here, we develop a method to extract multi-timescale expenditure patterns of consumers from a large dataset of scanned receipts. We use a non-negative tensor factorization (NTF) to detect intraand inter-week consumption patterns at one time. The proposed method allows us to characterize consumers based on their consumption patterns that are correlated over different timescales.

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Section: Related Workmentioning

confidence: 99%

Detecting multi-timescale consumption patterns from receipt data: a non-negative tensor factorization approach

et al. 2020

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“…Recommenders are pluggable components that process a recommendation request and provide a recommendation response that is delivered to the website and inserted in a carousel. For the current version of the platform, we have implemented two different algorithms well-known in the literature: 1) the Most Popular recommendation [1], and 2) the Item-Based Collaborative Filtering (IBCF) [3].…”

Section: Recommender Componentmentioning

confidence: 99%

ALARM: A Light Application for Recommendation and Monitoring

Batista¹,

Pereira²,

Domingues³

2020

Anais Estendidos Do XXVI Simpósio Brasileiro De Sistemas Multimídia E Web (WebMedia 2020)

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Big companies usually have human and financial resources to personalize their websites. On the other hand, small and medium-sized companies usually do not have such resources. In this paper we propose ALARM: A Light Application for Recommendation and Monitoring. This free platform enables automatic recommendations and monitoring in small and medium-sized websites. The platform is independent of the site structure, as well as monitoring and recommendation methods which may be used in it. We illustrate the features of the platform in a case study, where we show how it can be used to provide recommendations as well as to analyze them.

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“…Moreover, these platforms involve filtering algorithms (DeVito 2017) and recommendation systems that give disproportionate visibility to popular content within social circles. These mechanisms of algorithmic personalization have been largely debated in literature to understand if they affect the evolution of opinions (Rossi et al 2018;Bressan et al 2016) and polarize the network (Perra and Rocha 2019;Dandekar et al 2013;Geschke et al 2019), or if, conversely, they do not have a leading role in the formation of echo chambers (Möller et al 2018;Bakshy et al 2015).…”

Section: Online Information Consumption and Polarizationmentioning

confidence: 99%

Fact-checking strategies to limit urban legends spreading in a segregated society

Tambuscio

Ruffo

2019

Appl Netw Sci

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We propose a framework to study the spreading of urban legends, i.e., false stories that become persistent in a local popular culture, where social groups are naturally segregated by virtue of many (both mutable and immutable) attributes. The goal of this work is identifying and testing new strategies to restrain the dissemination of false information, focusing on the role of network polarization. Following the traditional approach in the study of information diffusion, we consider an epidemic network-based model where the agents can be 'infected' after being exposed to the urban legend or to its debunking depending on the belief of their neighborhood. Simulating the spreading process on several networks showing different kind of segregation, we perform a what-if analysis to compare strategies and to understand where it is better to locate eternal fact-checkers, nodes that maintain their position as debunkers of the given urban legend. Our results suggest that very few of these strategies have a chance to succeed. This apparently negative outcomes turns out to be somehow surprising taking into account that we ran our simulations under a highly pessimistic assumption, such that the 'believers', i.e., agents that accepted as true the urban legend after they have been exposed to it, will not change their belief no matter of how much external or internal additional informational sources they access to. This has implications on policies that are supposed to decide which strategy to apply to stop misinformation from spreading in real world networks.

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The Limits of Popularity-Based Recommendations, and the Role of Social Ties

Cited by 22 publications

References 35 publications

Detecting multi-timescale consumption patterns from receipt data: a non-negative tensor factorization approach

Detecting multi-timescale consumption patterns from receipt data: a non-negative tensor factorization approach

ALARM: A Light Application for Recommendation and Monitoring

Fact-checking strategies to limit urban legends spreading in a segregated society

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