Dynamics of movie competition and popularity spreading in recommender systems

Yeung, Chi Ho; Cimini, Giulio; Jin, Ci-Hang

doi:10.1103/physreve.83.016105

Cited by 11 publications

(12 citation statements)

References 12 publications

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“…opinions of past viewers and potential future viewers leads to a complex dynamics that agrees qualitatively with movie popularity behavior seen in real systems [229].…”

Section: Incoming Recommendationssupporting

confidence: 73%

Recommender systems

et al. 2012

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The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.

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“…opinions of past viewers and potential future viewers leads to a complex dynamics that agrees qualitatively with movie popularity behavior seen in real systems [229].…”

Section: Incoming Recommendationssupporting

confidence: 73%

Recommender systems

et al. 2012

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“…In our case, this comes about firstly because of the emergence of a strongly hierarchical distribution of high individual citation counts, such that the largest counts are finite fractions of the total sum; and secondly, because these largest counts fluctuate strongly between different histories. A somewhat similar phenomenon has been observed in a model for the dynamics of movie competition [46]: the late-time competition there observed between the best movies, characterised by very slow oscillations in their popularities, would yield a similar distribution f (Y ) to that of Figure 9. Conversely, we would also expect to see such slow oscillations between the dynamic fitnesses of the fittest papers in our model, in a given stochastic history.…”

Section: The Statistics Of Highest Citation Countssupporting

confidence: 74%

How the fittest compete for leadership: A tale of tails

Luck¹,

Mehta²

2017

Phys. Rev. E

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We investigate how leaders emerge as a consequence of the competitive dynamics between coupled papers in a model citation network. Every paper is allocated an initial fitness depending on its intrinsic quality. Its fitness then evolves dynamically as a consequence of the competition between itself and all the other papers in the field. It picks up citations as a result of this adaptive dynamics, becoming a leader if it has the highest citation count at a given time. Extensive analytical and numerical investigations of this model suggest the existence of a universal phase diagram, divided into regions of weak and strong coupling. In the former, we find an 'extended' and rather structureless distribution of citation counts among many fit papers; leaders are not necessarily those with the maximal fitness at any given time. By contrast, the strong-coupling region is characterised by a strongly hierarchical distribution of citation counts, that are 'localised' among only a few extremely fit papers, and exhibit strong history-to-history fluctuations, as a result of the complex dynamics among papers in the tail of the fitness distribution.

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“…The influence is amplified with successive recommendations. We note that such perspective is employed to explain the evolution movie popularity [10,11], which yields consistent predictions compared with observed data. It is thus interesting to examine such influence on recommender systems.…”

supporting

confidence: 60%

The reinforcing influence of recommendations on global diversification

Zeng¹,

Yeung²,

Shang³

et al. 2012

EPL

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Recommender systems are promising ways to filter the overabundant information in modern society. Their algorithms help individuals to explore decent items, but it is unclear how they allocate popularity among items. In this paper, we simulate successive recommendations and measure their influence on the dispersion of item popularity by Gini coefficient. Our result indicates that local diffusion and collaborative filtering reinforce the popularity of hot items, widening the popularity dispersion. On the other hand, the heat conduction algorithm increases the popularity of the niche items and generates smaller dispersion of item popularity. Simulations are compared to mean-field predictions. Our results suggest that recommender systems have reinforcing influence on global diversification.

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Dynamics of movie competition and popularity spreading in recommender systems

Cited by 11 publications

References 12 publications

Recommender systems

Recommender systems

How the fittest compete for leadership: A tale of tails

The reinforcing influence of recommendations on global diversification

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