Convolutional Collaborative Filter Network for Video Based Recommendation Systems

Convolutional Collaborative Filter Network for Video Based Recommendation Systems

Abstract

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.

\keywords

Video Convolutional Neural Network Recommendation System Hybrid Collaborative Filtering

1 Introduction

Understanding detailed audience composition is important for movie studios that invest in stories of uncertain commercial outcome. One source of uncertainty is that studios do now know how the movie is going to be like–what is it going to feel like–until the last few months before release. The other related source of uncertainty is audience and market fluidity. Studios don’t know with certainty ’how’, and especially ’which’ audiences are going to respond because the movie isn’t finished and because the strength and nature of competitive movies is also unknown.

An important function at movie studios is understanding the micro-segmentation of the customer base. E.g., not all super hero movies bring the same audience, etc. Over the last years, studios have invested in data tools to learn and to map out the customer segments, and to make predictions for future films. Granular predictions at the micro-segment level, and even at the customer level, have became routine inputs into important business decisions, and provide a trusted barometer of the potential financial performance of the movie.

Recommendation systems for movie theatrical releases have emerged as valuable tools that are especially well suited to provide granular forward looking audience projections to support greenlighting decisions, movie positioning studies, and marketing and distribution. MERLIN, the recommendation system for theatrical releases built at 20th Century Fox, is used to predict user attendance and segment indexes a year in advance, and to refine the prediction with anonymized user behavior signals.

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]. This finding can be explained by the different information content of the two media.

An open question when training video-based models is the choice of the feature space. A popular approach due to its simplicity is to analyze, individually, the different frames of a video using a trained image classification deep architecture. With this approach, the dense feature representations of the different frames can be pooled together in a single dense representation through an averaging or max element-wise operation. For example, Campo et al. [2] use the average pooling of image features of individual video frames and use it as the video features.

Video analysis using pooling schemes to collapse an entire video or part of a video into a unique dense feature vector can miss important semantic aspects of the video. Although simple to implement, the approach neglects the sequential and temporal aspects of the story, which, we argue, can be useful for characterization of a motion picture. For example, a trailer with a long close-up shot of a character is more likely for a drama movie than for an action movie, whereas a trailer with quick but frequent shots is more likely for an action movie. In both cases, though, average pooling would result in the same feature vector.

A second question when training video-based models is the level of semantic expressiveness of the model. One could use individual frames as unit of analysis, and apply image classification models to create a dense feature vector for each frame. Those vectors represent the ’objects’ depicted in the frames (car, face, etc), and can then be pooled together to create a video-level feature vector. A collaborative filter can use the presence of the objects in the trailer to predict customer behavior (e.g., maybe because the customer has seen movies in the past that also depicted some of those objects in the trailer).

Alternatively, one could try to use the sequences of ordered frames to identify patterns in the presence of objects that repeat themselves multiple times, perhaps at some regular time intervals, and perhaps across different trailers. Some of those sequences could indicate actual actions. For example, intermittent close-up shots of actors faces could be indicative of dialog, whereas intermittent shots of cars could be indicative of a car chase. One could feed those identified sequences, represented in a suitable dense vectorization, to a collaborative filter to determine whether the presence of some sequence in a trailer is predictive of customer behavior (e.g., maybe because the customer has seen movies in the past that also contained such sequence).

In this paper, we explore a temporal-aware model that takes temporal dynamics of movie trailer imagery into account to more faithfully capture salient elements that are conveyed not in individual frames, but over sequences of frames, and that hopefully create a better representation of the elements of the actual story. Our model is based on the idea of convolution over time [7]. As in previous work, for each video frame, we extract dense image features using a pre-trained image feature extractor. Then, we apply multi-layered convolution filters over the dense image features of multiple consecutive video frames. Convolutional filters are capable of capturing signals from not specific to individual frames, but that result from the combination of a sequence of frames that are in the filter receptive field. A longer receptive field can capture actions that unfold over longer periods of time. The convolution layer is followed by a temporal pooling layer to summarize the signals throughout the video before fed into a hybrid-collaborative filtering (CF) pipeline. The convolutional filters and collaborative filters are trained end-to-end and against millions of moviegoers’ attendance records. This allows them to learn video actions (or non-actions) that are most predictive of users’ movie preferences.

Figure 1: Overview of Merlin’s hybrid collaborative filtering pipeline. A logistic regression layer combines a distance-based CF model with user’s frequency and recency features to estimate the movie attendance probability. The model is trained end-to-end, and the loss of the logistic regression at the top is backpropogated to every trainable component.

2 Background

Movie recommendation for online streaming platforms has been well-studied in RecSys literature [3, 6, 5]. However, little research has been done to study the recommendation and prediction problems for theatrical releases. Specifically, the study of the cold-start prediction problem before and during movie production [9]. This paper is part of a series of works that report the development of Merlin, an experimental movie attendance prediction and recommendation system.

At its core, Merlin is a hybrid collaborative filtering pipeline that is enabled a fully anonymized, user privacy compliant, movie attendance dataset that combines data from different sources with hundreds of movies released over the last years, and millions of attendance records. Figure 1 shows a high-level overview of Merlin. Each movie in Merlin is modeled by a fixed-dimensional vector (referred to as movie vector in the figure) that are extracted from either movie synopsis or movie trailer data. Each user in Merlin is modeled by a user vector that is the sum of the movie vectors of the movies she attended, and the features from her basic demographics information.

The focus of this paper is at the bottom left of the figure, a video convolutional network architecture that is in charge of learning the components of the movie vector from the analysis of sequences in the video trailers. Please refer to [2] for a detailed description of the remaining part of the pipeline.

Figure 2: Video Convolutional Model for Trailers. denotes the number of trailers. Each trailer consists of 120 image frames (1 fps). 1024-dim image features are extracted by a pre-trained Inception V3 [10]. A convolutional layer with 1024 5-frame conv filters is applied, where each filter is of the shape (5 x 1024) and is applied over the temporal (i.e. frame) dimension. Next, a residual convolutional layer of the similar size is applied to further expand the receptive field of the model, which is followed by a average pooling layer to summarize the signals throughout a trailer.

3 Video Convolutional Model for Movie Trailers

The high level idea of the proposed video convolutional model is to learn a collection of filters, each of which captures a particular kind of object-sequence that could be suggestive of specific actions. For example, a pair of filters may learn a sequence of images of a country road and a car, which could be suggestive of someone driving down the country road; another pair of filters could learn intermittent sequences of a car and a person, which could indicate someone driving aggressively on the street and being chased, etc.

Assuming that certain object-sequences are universal across different movie trailers, and that different actions (and storylines) follow distinct object-sequence templates (for example, a complex car chasing action may ensue a car chase, followed by a car flipping, and a car explosion), the job of the network is to fit a object-specific temporal convolutional filters to learn such a object-sequence templates.

Apparently, there are countless variety of object-sequences that would appear in a movie trailer, and we will not have sufficient data to learn all of them. Our approach is to learn those that are relevant in the prediction problem from the customer transactional data. This is why we let movie attendance data guide the convolutional filters to focus on those actions that are most predictive of users’ preferences.

Figure 2 illustrates the proposed network structure. Specifically, the raw input to our video convolutional model are video frames extracted from the movie trailers. We down-sample the videos to 1 frame-per-second, and only use the first 120 seconds of the videos (i.e. each trailer has 120 frames of data). We first use a pre-trained Inception V3 model [10] to extract 1024 dimensional image features for each frame. Then, a convolution layer with 1024 convolutional filters are applied along the temporal dimension. Each filter is of the shape of , and has a receptive field of 5 frames. We apply these filters with stride = 3 and without padding, and therefore reduce the temporal dimensions to 39.

Then, we apply another 5-frame convolutional layer with skip connections. We use a residual-network design to expand the receptive field of the convolutional filters such that they can capture longer sequence of actions while, at the same time, preserving most signals from the previous convolutional layer. The effective receptive field of each filter after this layer is 17-frame long with a focus on the first 5 frames, which is sufficiently long to capture long sequences of actions.

Finally, an average pooling is applied to summarize the signals across the temporal dimension of a trailer. As shown in Figure 1, the output of average pooling will go through a multi-layer perception before it being used as the movie vector for the corresponding movie. As described earlier, the whole pipeline (including the CF part) is trained end-to-end to reduce the loss of prediction for user attendance. By doing this, we force the convolution filters to focus on those actions that are most predictive of users preferences.

Figure 3: Each row shows two sample video sequences from different trailers that highly activate a particular channel at the last relu layer (before entering the average pooling layer). These video sequences result in the activation that is at least two standard deviation higher than the average activation at their corresponding channels. These example suggests that the proposed video convolutional model captures scenes and actions across a wide variety of genres, including action, animation, romance, horror, monster, and war.

4 Performance Evaluation

We evaluate the proposed model using a fully anonymized, user privacy compliant, movie attendance dataset with millions of attendance records from hundreds of movies released over the last years. Among the latest 300 movie release, we hold out the attendance records of the most recent 50 movies for cold-start evaluation to simulate the prediction accuracy prior to the movie release. For the remaining movies, we random sample 80% of their attendance records for training, and hold out 10% for validation and another 10% for testing. All the models are trained until convergence on the validation set and evaluated on the testing set.

The model is trained using stochastic gradient descent with mini-batches. Every batch contains an mix of even number of positive and negative user-movie pairs. A positive user-movie represents that a user went to that particular movie according to our records, while a negative sample consists of a movie randomly sampled from all the movies that this user did not go to. For evaluation, for each movie, we use a 1-to-9 positive-negative ratio to simulate the actual average movie attendance rate.

4.1 Evaluation Results

We compared the proposed video convoultional model to the following baselines: 1) Merlin + Text that uses movie synopses vectorized via a word2vec model as movie features [2]. 2) Merlin + Video AvgPool a temporal-unaware model that takes the average image features extracted by a Inception V3 model as the movie features. To have a fair comparison, we apply the same video-preprocessing steps, including downsampling and limiting the length to 120 frames, to both Merlin + Video AvgPool and the proposed Merlin + Video Convolution.

We use Area-Under-Curve (AUC) [4] as our performance metric. As our ultimate goal is to determine what kind of moviegoers a movie will attract, and provide insights for movie production, AUC serves a better indicator to the overall prediction power of the models than the per-user ranking metrics, such as Top-k recall.

Table 1 summarizes the evaluation results. As shown, every model shows similar performance for the In-Matrix movies, where the existing attendance records are available. This is not surprising because the in-matrix accuracy is mostly determined by the collaborative filtering part of the model. On the other hand, the models differ in their Cold-Start performances (i.e. for movies that do not have attendance records). Specifically, the proposed Merlin + Video Convolution outperforms the Merlin + Text and Merlin + Video AvgPool by 2 and almost 3 percentage points respectively. The fact that the proposed convolution-based model outperforms the AvgPool-based model suggests that the proposed convolutional architecture is a more effective way to extract video features that are predictive of users’ preferences. On the other hand, the fact that our video-based model is able to outperform the existing text-based model hint a new research avenue into utilizing more and richer multimedia contents to improve the movie recommendations.

4.2 Model Explanability

By looking at the activation of convolutional filters, the proposed video convolutional model also provide a way for us to examine the object-sequence templates our model actually captures that is not possible previously with an AvgPool method. Figure 3 show sample video sequences that highly activate different channels at the last relu layer (before entering the average pooling layer). These example suggests that the proposed video convolutional model captures many typical object-sequences across wide variety of genres, including action, animation, romance, horror, monster, and war. For example, Channel 4 appears to be activated by the slow and intense close-up shots that are typical in the horror movies.For another example, Channel 8 appears to be activated by stunt performance that include a flipping vehicles that are typical in action movies or superhero movies.

Model In Matrix Cold Start
Merlin + Text 0.849 0.731
Merlin + Video AvgPool 0.845 0.723
Merlin + Video Convolution 0.849 0.751
Table 1: Area-Under-Curve of Attendance Prediction

5 Discussion

This work presents the analysis of temporal sequences of objects that appear in movie trailers when applied to the problem of movie recommendation and audience prediction.

Movie trailers are engineered to increase awareness and to present key aspects of the story and the cinematography. Filmmakers and studios use movie trailers to increase the urge to see the movie. To do that, they rely on proven techniques and templates to create trailers that maximize intent to watch while at the same time ensuring that the unique elements of the story–what makes the movie worth seeing–get appropriately reflected in the trailer. Every trailer is different, but there are commonalities between trailers of movies that belong to the same genre.

The temporal sequencing of elements in a movie trailer (e.g., when to introduce a character, for how long, etc) is an aspect that filmmakers and studios pay careful attention to because of the short format of the trailer. As with other aspects of the trailer, temporal sequencing follows norms that are tried and tested. Moreover, the templates used for different types of movies are also different. Moviegoers decisions about which movies to go see can be projected on the feature space of movie trailers to create an implicit measure of similarity or dissimilarity between trailers. A moviegoer that buys tickets to a movie has probably seen movies in the past the trailers of which contained similar sequences. When we characterize trailers using object-sequences, as we do here, moviegoers actions are implicitly telling us which object-sequences matter to measure trailer similarity.

Our results show that recommendation systems that are based on the analysis of object-sequences have more predictive power in cold start situations than systems based on average pooling of video frames. Object-sequences are more effective at predicting customer behavior because they provide a more efficient way to represent trailers than simple average pooling. They are more efficient because they use convolutional filters to learn which distinct temporal sequences are optimal for each of the 1024 dimensions of the frame embeddings, and use collaborative filters to learn which non-linear combination of sequences is optimal for prediction. The resulting convolutional and collaborative network architecture contributes not only to isolate the specific components of the video signal that are more helpful for the prediction problem, but to increase the explain-ability of the model predictions.

References

  1. Miguel Campo, JJ Espinoza, Julie Rieger, and Abhinav Taliyan. Collaborative metric learning recommendation system: Application to theatrical movie releases. arXiv preprint arXiv:1803.00202, 2018.
  2. Miguel Campo, Cheng-Kang Hsieh, Matt Nickens, JJ Espinoza, Abhinav Taliyan, Julie Rieger, Jean Ho, and Bettina Sherick. Competitive analysis system for theatrical movie releases based on movie trailer deep video representation. arXiv preprint arXiv:1807.04465, 2018.
  3. Paul Covington, Jay Adams, and Emre Sargin. Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems, New York, NY, USA, 2016.
  4. James A Hanley and Barbara J McNeil. The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology, 143(1):29–36, 1982.
  5. F Maxwell Harper and Joseph A Konstan. The movielens datasets: History and context. ACM Transactions on Interactive Intelligent Systems (TiiS), 5(4):19, 2016.
  6. Yehuda Koren, Robert Bell, and Chris Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8), 2009.
  7. Yann LeCun and Yoshua Bengio. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361(10):1995, 1995.
  8. Joonseok Lee, Sami Abu-El-Haija, Balakrishnan Varadarajan, and Apostol Paul Natsev. Collaborative deep metric learning for video understanding. pages 481–490, 2018.
  9. Andrew I Schein, Alexandrin Popescul, Lyle H Ungar, and David M Pennock. Methods and metrics for cold-start recommendations. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pages 253–260. ACM, 2002.
  10. Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818–2826, 2016.
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