Collaborative Metric Learning Recommendation System: Application to Theatrical Movie Releases

Campo, Miguel del; Espinoza, J. J.; Rieger, Julie; Taliyan, Abhinav

doi:10.48550/arxiv.1803.00202

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indeed, natural language processing methods have been used to computationally study and classify films [78][79][80][81], film reviews, being an important industry feedback component [82][83][84], social media coverage of festivals [85], and gender bias in synopses and scripts [86]. Inferring and making use of film metadata and characteristics [87], as well as viewer activity [88,89], has been a central interest of recommendation systems research, more so with the commercial importance stemming from increasing platformization, and digitalization of the film industry, in general [90]. In contrast to most of the aforementioned research and to the survey-focused event and festival research [9], here we focus on festivals as the primary unit of analysis but use available film metadata to construct festival profiles.…”

Section: Quantifying Festivals Using Metadata Embeddingsmentioning

confidence: 99%

Quantifying the global film festival circuit: Networks, diversity, and public value creation

Zemaityte,

Karjus,

Rohn

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Film festivals are a key component in the global film industry in terms of trendsetting, publicity, trade, and collaboration. We present an unprecedented analysis of the international film festival circuit, which has so far remained relatively understudied quantitatively, partly due to the limited availability of suitable data sets. We use large-scale data from the Cinando platform of the Cannes Film Market, widely used by industry professionals. We explicitly model festival events as a global network connected by shared films and quantify festivals as aggregates of the metadata of their showcased films. Importantly, we argue against using simple count distributions for discrete labels such as language or production country, as such categories are typically not equidistant. Rather, we propose embedding them in continuous latent vector spaces. We demonstrate how these “festival embeddings” provide insight into changes in programmed content over time, predict festival connections, and can be used to measure diversity in film festival programming across various cultural, social, and geographical variables—which all constitute an aspect of public value creation by film festivals. Our results provide a novel mapping of the film festival circuit between 2009–2021 (616 festivals, 31,989 unique films), highlighting festival types that occupy specific niches, diverse series, and those that evolve over time. We also discuss how these quantitative findings fit into media studies and research on public value creation by cultural industries. With festivals occupying a central position in the film industry, investigations into the data they generate hold opportunities for researchers to better understand industry dynamics and cultural impact, and for organizers, policymakers, and industry actors to make more informed, data-driven decisions. We hope our proposed methodological approach to festival data paves way for more comprehensive film festival studies and large-scale quantitative cultural event analytics in general.

show abstract

Section: Quantifying Festivals Using Metadata Embeddingsmentioning

confidence: 99%

Quantifying the global film festival circuit: Networks, diversity, and public value creation

Zemaityte,

Karjus,

Rohn

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Indeed, natural language processing methods have been used to computationally study and classify films [66][67][68][69], film reviews, being an important industry feedback component [70][71][72], social media coverage of festivals [73], and gender bias in synopses and scripts [74]. Inferring and making use of film metadata and characteristics [75], as well as viewer activity [76,77], has been a central interest of recommendation systems research, more so with the commercial importance stemming from increasing platformization, and digitalization of the film industry in general [78]. In contrast to most of the aforementioned research, and to the survey-focused event and festival research [9], here we focus on festivals as the primary unit of analysis but use available film metadata to construct festival profiles.…”

Section: Quantifying Festivals Using Metadata Embeddingsmentioning

confidence: 99%

Quantifying the global film festival circuit: Networks, diversity, and public value creation

Zemaityte¹,

Karjus²,

Rohn³

et al. 2023

Preprint

View full text Add to dashboard Cite

Film festivals are a key component in the global film industry in terms of trendsetting, publicity, trade, and collaboration. We present an unprecedented analysis of the international film festival circuit, which has so far remained relatively understudied quantitatively, partly due to the limited availability of suitable data sets. We use large-scale data from the Cinando platform of the Cannes Film Market, widely used by industry professionals. We explicitly model festival events as a global network connected by shared films, and quantify festivals as aggregates of the metadata of their showcased films. Importantly, we argue against using simple count distributions for discrete labels such as language or production country, as such categories are typically not equidistant. Rather, we propose embedding them in continuous latent vector spaces. We demonstrate how these “festival embeddings” provide insight into changes in programmed content over time, predict festival connections, and can be used to measure diversity in film festival programming across various cultural, social, and geographical variables---which all constitute an aspect of public value creation by film festivals. Our results provide a novel mapping of the film festival circuit between 2009--2021 (616 festivals, 31,989 unique films), highlighting festival types that occupy specific niches, diverse series, and those that evolve over time. We also discuss how these quantitative findings fit into media studies and research on public value creation by cultural industries. With festivals occupying a central position in the film industry, investigations into the data they generate hold opportunities for researchers to better understand industry dynamics and cultural impact, and for organizers, policymakers, and industry actors to make more informed, data-driven decisions. We hope our proposed methodological approach to festival data paves way for more comprehensive film festival studies and large-scale quantitative cultural event analytics in general.

show abstract

“…Predicting user behavior far in advance of the movie release is an example of pure cold-start prediction and is challenging for movies that are novel, movies that are non-sequels, and movies that cross traditional genres. Recent research has explored using movie synopses [1] and movie trailers ( [2], [8]), combined with collaborative filter models, to predict which customers consume which movies. In our analysis, Campo et al showed that recommendations made based on video data are qualitatively different from those based on the synopsis data [2].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Collaborative Filter Network for Video Based Recommendation Systems

Hsieh,

Campo,

Taliyan

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

This analysis explores the temporal sequencing of objects in a movie trailer. Temporal sequencing of objects in a movie trailer (e.g., a long shot of an object vs intermittent short shots) can convey information about the type of movie, plot of the movie, role of the main characters, and the filmmakers cinematographic choices. When combined with historical customer data, sequencing analysis can be used to improve predictions of customer behavior. E.g., a customer buys tickets to a new movie and maybe the customer has seen movies in the past that contained similar sequences. To explore object sequencing in movie trailers, we propose a video convolutional network to capture actions and scenes that are predictive of customers' preferences. The model learns the specific nature of sequences for different types of objects (e.g., cars vs faces), and the role of sequences in predicting customer future behavior. We show how such a temporal-aware model outperforms simple feature pooling methods proposed in our previous works and, importantly, demonstrate the additional model explain-ability allowed by such a model.

show abstract

Collaborative Metric Learning Recommendation System: Application to Theatrical Movie Releases

Cited by 3 publications

References 0 publications

Quantifying the global film festival circuit: Networks, diversity, and public value creation

Quantifying the global film festival circuit: Networks, diversity, and public value creation

Quantifying the global film festival circuit: Networks, diversity, and public value creation

Convolutional Collaborative Filter Network for Video Based Recommendation Systems

Contact Info

Product

Resources

About