• Corpus ID: 49664782

Competitive Analysis System for Theatrical Movie Releases Based on Movie Trailer Deep Video Representation

  title={Competitive Analysis System for Theatrical Movie Releases Based on Movie Trailer Deep Video Representation},
  author={Miguel Campo and Cheng-Kang Hsieh and Matt Nickens and J. J. Espinoza and Abhinav Taliyan and Julie Rieger and Jean Ho and Bettina Sherick},
Audience discovery is an important activity at major movie studios. Deep models that use convolutional networks to extract frame-by-frame features of a movie trailer and represent it in a form that is suitable for prediction are now possible thanks to the availability of pre-built feature extractors trained on large image datasets. Using these pre-built feature extractors, we are able to process hundreds of publicly available movie trailers, extract frame-by-frame low level features (e.g., a… 

Figures and Tables from this paper

Leveraging analytics to produce compelling and profitable film content

A conceptual framework of key analytic techniques that film producers may engage throughout the production process, such as script analytics, talent analytics, and audience analytics is proposed.

TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learning

This paper presents a vertically integrated hardware/software co-design, which includes a custom DIMM module enhanced with near-memory processing cores tailored for DL tensor operations, populated inside a GPU-centric system interconnect as a remote memory pool.

MERCI: efficient embedding reduction on commodity hardware via sub-query memoization

Deep neural networks (DNNs) with embedding layers are widely adopted to capture complex relationships among entities within a dataset. Embedding layers aggregate multiple embeddings — a dense vector

Near-Memory Processing in Action: Accelerating Personalized Recommendation With AxDIMM

This work developed a scalable, practical DIMM-based NMP solution tailor-designed for accelerating the inference serving of personalized recommendation system using industry-representative recommendation framework and experimentally validated the performance of a two-ranked AxDIMM prototype.

Centaur: A Chiplet-based, Hybrid Sparse-Dense Accelerator for Personalized Recommendations

Centaur is presented, a chiplet-based hybrid sparse-dense accelerator that addresses both the memory throughput challenges of embedding layers and the compute limitations of MLP layers.

Convolutional Collaborative Filter Network for Video Based Recommendation Systems

This analysis explores the temporal sequencing of objects in a movie trailer using a video convolutional network to capture actions and scenes that are predictive of customers' preferences and shows how such a temporal-aware model outperforms simple feature pooling methods proposed in previous works.

RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing

  • Liu KeUdit Gupta Xiaodong Wang
  • Computer Science
    2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA)
  • 2020
RecNMP is proposed which provides a scalable solution to improve system throughput, supporting a broad range of sparse embedding models, and is specifically tailored to production environments with heavy co-location of operators on a single server.



Content-Based Video Recommendation System Based on Stylistic Visual Features

A new content-based recommender system that encompasses a technique to automatically analyze video contents and to extract a set of representative stylistic features grounded on existing approaches of Applied Media Theory, to improve the accuracy of recommendations.

Collaborative Metric Learning Recommendation System: Application to Theatrical Movie Releases

A system based on Collaborative (Deep) Metric Learning (CML) to predict the purchase probabilities of new theatrical releases, using a large dataset of customer histories and testing the model for a set of movies that were released outside of the training window.

YouTube-8M: A Large-Scale Video Classification Benchmark

YouTube-8M is introduced, the largest multi-label video classification dataset, composed of ~8 million videos (500K hours of video), annotated with a vocabulary of 4800 visual entities, and various (modest) classification models are trained on the dataset.

The MovieLens Datasets: History and Context

The history of MovieLens and the MovieLens datasets is documents, including a discussion of lessons learned from running a long-standing, live research platform from the perspective of a research organization, and best practices and limitations of using the Movie Lens datasets in new research are documented.

Toward Building a Content-Based Video Recommendation System Based on Low-Level Features

One of the challenges in video recommendation systems is the New Item problem, which happens when the system is unable to recommend video items, that no information is available about them. For

Deep Neural Networks for YouTube Recommendations

This paper details a deep candidate generation model and then describes a separate deep ranking model and provides practical lessons and insights derived from designing, iterating and maintaining a massive recommendation system with enormous user-facing impact.

Collaborative Variational Autoencoder for Recommender Systems

A Bayesian generative model called collaborative variational autoencoder (CVAE) that considers both rating and content for recommendation in multimedia scenario that is able to significantly outperform the state-of-the-art recommendation methods with more robust performance is proposed.

DropoutNet: Addressing Cold Start in Recommender Systems

This work proposes a neural network based latent model called DropoutNet to address the cold start problem in recommender systems and shows that neural network models can be explicitly trained for cold start through dropout.

Going deeper with convolutions

We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition

Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems

A novel hierarchical Bayesian model which jointly incorporates topic modeling and probabilistic matrix factorization of social networks is proposed which is able to automatically infer useful latent topics and social information as well as their importance to collaborative filtering from the training data.