Machine learning predicts movie genres from summaries with high accuracy.
problem Predicting movie genres from plot summaries.
method Used Naive Bayes, Word2Vec+XGBoost, Recurrent Neural Networks, and Gated Recurrent Units (GRU) for text classification and multi-label problem.
result GRU neural networks achieve the best result with a Jaccard Index of 50.0%, F-score of 0.56, and hit rate of 80.5%.
Paper classifies movie genres using multimodal data.
problem Challenging task of multi-label movie genre classification.
method Created dataset from video clips, subtitles, synopses, and posters. Extracted features using various descriptors. Evaluated using different classifiers and late fusion strategy.
result Best F-Score result of 0.628 achieved by combining LSTM on synopses and CNN on movie trailer frames.
This paper presents a novel model for multimodal learning based on gated neural networks. The Gated Multimodal Unit (GMU) model is intended to be used as an internal unit in a neural network architecture whose purpose is to find an intermediate representation based on a combination of data from different modalities. Th…
A model predicts user movie preferences based on novelty-seeking traits.
problem Accurately predicting user movie preferences for competitive websites.
method DFNSM model uses demographic, genre, and novelty-seeking data.
result DFNSM outperforms previous models in movie recommendation accuracy.
Improved ranking method for scarce data with feature info.
problem Ranking items with limited comparisons and feature data.
method Modified RankCentrality using diffusion methods for feature info.
result Meaningful rankings even with scarce comparisons.
Music genre classification is an essential tool for music information retrieval systems and it has been finding critical applications in various media platforms. Two important problems of the automatic music genre classification are feature extraction and classifier design. This paper investigates inter-genre similarit…
We introduce normalized nonnegative models (NNM) for explorative data analysis. NNMs are partial convexifications of models from probability theory. We demonstrate their value at the example of item recommendation. We show that NNM-based recommender systems satisfy three criteria that all recommender systems should ide…
Consider a movie recommendation system where apart from the ratings information, side information such as user's age or movie's genre is also available. Unlike standard matrix completion, in this setting one should be able to predict inductively on new users/movies. In this paper, we study the problem of inductive matr…
The study uses music chords to predict Brazilian music genres.
problem Classifying popular Brazilian music genres based on harmonic structures.
method Extracted and engineered harmonically related features from chords data, used random forest model for classification.
result Features from harmonic elements can predict Brazilian music genres.
Challenge aims to recognize music genres from audio.
problem Recognizing music genres from audio recordings.
method Open data challenge with submissions evaluated.
result Results presented from the challenge.
New method predicts target genres from source genres, unifying music tag systems.
problem Automatic genre inference fails to handle music genre diversity and subjectivity.
method Knowledge-based, statistical, and hybrid translation models.
result Hybrid translation model is most effective for multilabel classification.
Transfer learning artist group factors for more accurate music genre classification.
problem Automated music genre classification from audio is challenging due to subjective and noisy genre labels.
method Apply transfer learning to learn artist-related factors for genre classification, using different transfer methods.
result Achieved highest validation accuracy on FMA dataset through multi-task learning.
Paper classifies Brazilian music genres using song lyrics with BLSTM network.
problem Classifying Brazilian music genres from lyrics.
method Used BLSTM network combined with SVM, Random Forest, and word embeddings.
result BLSTM outperforms other models with an F1-score of 0.48.
Graph neural networks improve music genre classification on audio datasets.
problem Difficulty in applying deep learning on spectrograms due to lack of quality data and augmentation.
method Combination of CNN and Graph Neural Networks (GNN) with Siamese Neural Networks.
result Achieved state-of-the-art results on GTZAN and AudioSet datasets.
Paper applies GANs to symbolic music genre transfer.
problem Symbolic music genre transfer using GANs.
method CycleGAN architecture with additional discriminators to preserve structure.
result Fidelity of transformed music improved with additional discriminators.
Bipartite networks are currently regarded as providing a major insight into the organization of many real-world systems, unveiling the mechanisms driving the interactions occurring between distinct groups of nodes. One of the most important issues encountered when modeling bipartite networks is devising a way to obtain…
Sparse VAE learns latent factors from high-dimensional data.
problem Unsupervised representation learning on high-dimensional data.
method Sparse VAE model that learns latent factors summarizing data associations.
result Sparse VAE can recover true model parameters with infinite data.
For proper subsets U of {1,2,...,31} we define and construct U-regular isotopy invariants of the Carter-Rieger-Saito movies (representing knotted surfaces) and use these to show that there are ambient isotopic knotted surfaces represented by movies M1 and M2 for which movie-moves of type 31 are required to get from M1 …
MCLNN improves music genre classification with automated feature exploration.
problem Music genre classification using neural networks.
method MCLNN uses a mask to enforce sparseness and learn time-frequency representations.
result MCLNN achieves competitive accuracy compared to state-of-the-art methods.
Neural nets classify Thai Lukthung songs from other genres.
problem Classifying Thai Lukthung songs for personalized music recommendations.
method Developed neural networks using lyrics and audio features.
result Combined model yields F1 score of 0.86 for Lukthung classification. GASC models semantic change in Ancient Greek texts using genre metadata.
problem Associating correct meanings in historical Ancient Greek texts.
method Develops a dynamic semantic change model leveraging genre metadata.
result Improves predictive performance on semantic change in Ancient Greek texts.
IGMC learns inductive matrix completion without side info.
problem Inductive matrix completion without side information.
method Graph Neural Network (GNN) trained on 1-hop subgraphs of the rating matrix.
result Achieves competitive performance with state-of-the-art transductive baselines.
Predicting movie box office success using historical data and modern computing.
problem Manual prediction of movie revenue is difficult due to many exogenous variables.
method Use modern computing power and historical data to model movie revenue.
result Predicted movie revenues can be used for planning production and distribution stages.
Enhanced feature extraction pipeline boosts music genre recognition.
problem Improving music genre classification accuracy.
method Extended feature engineering pipeline with multiple stages and feedback loops.
result The method significantly improves classification performance on the GTZAN dataset.
We present a grid diagram analogue of Carter, Rieger and Saito's smooth movie theorem. Specifically, we give definitions for grid movies, grid movie isotopies and present a definition of grid planar isotopy as a particular subset of the grid diagram moves: stabilization, destabilization and commutation. We show that gr…
A movie multilayer network model captures narration from script, subtitles, and content.
problem Discovering content and stories in movies using network models.
method Developed a multilayer network model using visual and textual semantic cues.
result Demonstrated the effectiveness of the model on the Star Wars saga.
Hybrid VAE improves movie recommendation accuracy.
problem Improving personalized recommendations in online marketplaces.
method Combining movie embeddings from a sibling VAE network with user ratings for movie recommendation.
result Empirical evidence shows VAE network benefits from incorporating movie embeddings.
A shadow diagram is a knot diagram with under-over information omitted; a shadow movie is a sequence of shadow diagrams related by shadow Reidemeister moves. We show that not every shadow movie arises as the shadow of a Reidemeister movie, meaning a sequence of classical knot diagrams related by classical Reidemeister …
Feature extraction has gained increasing attention in the field of machine learning, as in order to detect patterns, extract information, or predict future observations from big data, the urge of informative features is crucial. The process of extracting features is highly linked to dimensionality reduction as it impli…
Study of equivariant movie moves for involutive links.
problem Equivariant cobordisms between involutive links.
method Equivariant Morse theory and singularity theory.
result 39 equivariant movie moves for isotopic cobordisms.
Two singular links are cobordant if one can be obtained from the other by singular link isotopy together with a combination of births or deaths of simple unknotted curves, and saddle point transformations. A movie description of a singular link cobordism in 4-space is a sequence of singular link diagrams obtained from …
GWCA analyzes cross-graph correlations for movie retrieval.
problem Cross heterogeneous graph comparison in movie retrieval.
method Spectral graph filtering, Wasserstein metric learning, generalized eigenvalue decomposition.
result Surprise consistency in learning processes and closed-form solution.
We present a marked analogue of Carter and Saito's movie theorem. Our definition of marking was chosen to coincide with the markings that arise in link Floer homology. In order to deal with complications arising from certain isotopies, we define three equivalence relations for marked surfaces and work over an equivalen…
A new method for better recommendation by accounting for hidden factors.
problem Hidden factors affecting both watched movies and ratings.
method Two-stage probabilistic models to remove bias due to confounding.
result Improved recommendation and stable performance against interventions.
BERT models can classify multilingual party manifestos across different dimensions.
problem Costly manual annotation of large corpora in social science.
method Domain transfer across geographical locations, languages, time, and genre using fine-tuned transformer models.
result BERT models can be applied to future data with similar performance.
Study uses EEG and ML to predict movie ratings with 72% accuracy.
problem Predicting consumer preferences for movie trailers.
method EEG and machine learning techniques to analyze brain responses to movie trailers.
result Predicted movie ratings with 72% accuracy.
Physicists use quantum models to describe the behavior of physical systems. Quantum models owe their success to their interpretability, to their relation to probabilistic models (quantization of classical models) and to their high predictive power. Beyond physics, these properties are valuable in general data science. …
Improved video and movie description using multitask learning.
problem Lack of training data and poor generalization in video captioning.
method Multitask learning encoder-decoder framework for video sequences.
result Improved performance on multi-caption and single-caption datasets.
A fast method for fitting deep hierarchical models to large datasets.
problem Fitting a deeply-nested hierarchical model to a large book review dataset.
method Moment-based estimator extension for arbitrarily deep hierarchies.
result Orders of magnitude faster than standard maximum likelihood procedures.
Study analyzes IMDB movie comments and Twitter data using machine learning and vector space techniques.
problem Sentiment analysis of IMDB movie comments and Twitter data.
method Created a vector space in KNIME Analytics platform, used Decision Trees, Naïve Bayes, and SVM algorithms for classification.
result SVM algorithm provided the best classification results for both IMDB movie comments and Twitter data sets.
MCLNN improves music genre classification by learning frequency bands.
problem Classifying music genres using neural networks adapted from image recognition.
method MCLNN learns frequency bands, reducing susceptibility to frequency shifts and enabling concurrent exploration of feature combinations.
result MCLNN outperforms state-of-the-art Convolutional Neural Networks on the Ballroom music dataset.
The paper compares and improves aggregators for relational probabilistic models.
problem Predicting gender from movie ratings is challenging due to varying numbers of movies per user and users per movie.
method The paper compares existing aggregators and proposes new ones, showing their empirical superiority.
result New aggregators and model modifications outperform existing ones.
Solves the film scheduling and staggered showtimes problem for movie theaters.
problem Maximize attendance and revenue by scheduling films with staggered showtimes.
method Binary integer linear optimization to find optimal schedules for each cluster of neighboring locations.
result Optimal scheduling cannot be done for all locations at once, but must be done for each cluster.
We show that for any hyperbolic surface of genus g, the eigenvalue λ2g−2 of the Laplace operator is > 1/4.
Adapting momentum from optimization to reinforcement learning.
problem Improving the convergence and stability of reinforcement learning algorithms.
method Introducing Momentum Value Iteration (MoVI) by incorporating an average of consecutive state-action value functions, inspired by the concept of momentum in optimization.
result MoVI improves the convergence and stability of reinforcement learning algorithms, as demonstrated by experiments on Atari games.
The paper proposes a new method for user-movie recommendation systems.
problem Improving recommendation accuracy in collaborative filtering.
method Uses Empirical Bayes with Reversible Jump Markov Chain in a Bayesian setup.
result Demonstrates improved hyper-parameter tuning and recommendation accuracy.
Generative methods for creating new items for user groups.
problem Creating new items for groups of users with varying preferences.
method Formalized joint problem, used VAE latent space for item generation and user group prediction.
result Generated items similar to highly desirable unobserved items.
27 problems identified in automating movie/TV subtitle translation.
problem Challenges in translating movie/TV subtitles.
method Categorized problems into three categories and evaluated translation quality.
result Frontier NLP systems struggle with subtitles and require post-processing.