scholarly journals Sequence-Based Explainable Hybrid Song Recommendation

2021 ◽  
Vol 4 ◽  
Author(s):  
Khalil Damak ◽  
Olfa Nasraoui ◽  
William Scott Sanders
Keyword(s):  
Deep Learning ◽  
Cold Start ◽  
Musical Instrument ◽  
Learning Sequence ◽  
Digital Interface ◽  
Top Performers ◽  
Recommendation Accuracy ◽  
Deep Learning Model ◽  
Cold Start Problem ◽  
Validation Experiments

Despite advances in deep learning methods for song recommendation, most existing methods do not take advantage of the sequential nature of song content. In addition, there is a lack of methods that can explain their predictions using the content of recommended songs and only a few approaches can handle the item cold start problem. In this work, we propose a hybrid deep learning model that uses collaborative filtering (CF) and deep learning sequence models on the Musical Instrument Digital Interface (MIDI) content of songs to provide accurate recommendations, while also being able to generate a relevant, personalized explanation for each recommended song. Compared to state-of-the-art methods, our validation experiments showed that in addition to generating explainable recommendations, our model stood out among the top performers in terms of recommendation accuracy and the ability to handle the item cold start problem. Moreover, validation shows that our personalized explanations capture properties that are in accordance with the user’s preferences.

scholarly journals Preference Mining Using Neighborhood Rough Set Model on Two Universes

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Kai Zeng
Keyword(s):  
Rough Set ◽  
User Satisfaction ◽  
Traditional Method ◽  
Cold Start ◽  
Effective Solution ◽  
Neighborhood Rough Set ◽  
Recommendation Accuracy ◽  
Item Data ◽  
Two Universes ◽  
Cold Start Problem

Preference mining plays an important role in e-commerce and video websites for enhancing user satisfaction and loyalty. Some classical methods are not available for the cold-start problem when the user or the item is new. In this paper, we propose a new model, called parametric neighborhood rough set on two universes (NRSTU), to describe the user and item data structures. Furthermore, the neighborhood lower approximation operator is used for defining the preference rules. Then, we provide the means for recommending items to users by using these rules. Finally, we give an experimental example to show the details of NRSTU-based preference mining for cold-start problem. The parameters of the model are also discussed. The experimental results show that the proposed method presents an effective solution for preference mining. In particular, NRSTU improves the recommendation accuracy by about 19% compared to the traditional method.

scholarly journals Simple and Efficient Computational Intelligence Strategies for Effective Collaborative Decisions

2019 ◽  
Vol 11 (1) ◽  
pp. 24
Author(s):  
Emelia Opoku Aboagye ◽  
Rajesh Kumar
Keyword(s):  
Deep Learning ◽  
Feature Space ◽  
Cold Start ◽  
Personalized Recommendation ◽  
Tensor Factorization ◽  
Scalable Systems ◽  
Collaborative Recommendation ◽  
Real World Datasets ◽  
Low Dimensional ◽  
Cold Start Problem

We approach scalability and cold start problems of collaborative recommendation in this paper. An intelligent hybrid filtering framework that maximizes feature engineering and solves cold start problem for personalized recommendation based on deep learning is proposed in this paper. Present e-commerce sites mainly recommend pertinent items or products to a lot of users through personalized recommendation. Such personalization depends on large extent on scalable systems which strategically responds promptly to the request of the numerous users accessing the site (new users). Tensor Factorization (TF) provides scalable and accurate approach for collaborative filtering in such environments. In this paper, we propose a hybrid-based system to address scalability problems in such environments. We propose to use a multi-task approach which represent multiview data from users, according to their purchasing and rating history. We use a Deep Learning approach to map item and user inter-relationship to a low dimensional feature space where item-user resemblance and their preferred items is maximized. The evaluation results from real world datasets show that, our novel deep learning multitask tensor factorization (NeuralFil) analysis is computationally less expensive, scalable and addresses the cold-start problem through explicit multi-task approach for optimal recommendation decision making.

scholarly journals Research on Music Classification Technology Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Fang Zhang
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Musical Instrument ◽  
Digital Audio ◽  
Digital Interface ◽  
Classifier Design ◽  
Music Classification ◽  
Interdisciplinary Field ◽  
Shallow Structure ◽  
Mc Method

With the advent of the digital music era, digital audio sources have exploded. Music classification (MC) is the basis of managing massive music resources. In this paper, we propose a MC method based on deep learning to improve feature extraction and classifier design based on MIDI (musical instrument digital interface) MC task. Considering that the existing classification technology is limited by the shallow structure, it is difficult for the classifier to learn the time sequence and semantic information of music; this paper proposes a MIDIMC method based on deep learning. In the experiment, we use the MC method proposed in this paper to achieve 90.1% classification accuracy, which is better than the existing classification method based on BP neural network, and verify the music with its classification accuracy. The key point is that the music division method used in this paper has correct MC efficiency. However, due to the limited ability and time involved in the interdisciplinary field, the methodology of this paper has certain limitations, which still needs further research and improvement.

scholarly journals A Deep Temporal Neural Music Recommendation Model Utilizing Music and User Metadata

2019 ◽  
Vol 9 (4) ◽  
pp. 703 ◽  
Author(s):  
Hai-Tao Zheng ◽  
Jin-Yuan Chen ◽  
Nan Liang ◽  
Arun Sangaiah ◽  
Yong Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Short Term Memory ◽  
Dimensional Space ◽  
Cold Start ◽  
Music Recommendation ◽  
Short Term ◽  
User Coverage ◽  
Cold Start Problem ◽  
Temporal Preferences

Deep learning shows its superiority in many domains such as computing vision, nature language processing, and speech recognition. In music recommendation, most deep learning-based methods focus on learning users’ temporal preferences using their listening histories. The cold start problem is not addressed, however, and the music characteristics are not fully exploited by these methods. In addition, the music characteristics and the users’ temporal preferences are not combined naturally, which cause the relatively low performance of music recommendation. To address these issues, we proposed a Deep Temporal Neural Music Recommendation model (DTNMR) based on music characteristics and the users’ temporal preferences. We encoded the music metadata into one-hot vectors and utilized the Deep Neural Network to project the music vectors to low-dimensional space and obtain the music characteristics. In addition, Long Short-Term Memory (LSTM) neural networks are utilized to learn about users’ long-term and short-term preferences from their listening histories. DTNMR alleviates the cold start problem in the item side using the music medadata and discovers new users’ preferences immediately after they listen to music. The experimental results show DTNMR outperforms seven baseline methods in terms of recall, precision, f-measure, MAP, user coverage and AUC.

scholarly journals Pedestrian Re-Recognition Algorithm Based on Optimization Deep Learning-Sequence Memory Model

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Feng-Ping An
Keyword(s):  
Deep Learning ◽  
Recognition Accuracy ◽  
Image Sequence ◽  
Learning Model ◽  
Recognition Algorithm ◽  
Learning Sequence ◽  
Recognition Algorithms ◽  
Sequence Memory ◽  
Adaptive Ability ◽  
Deep Learning Model

Pedestrian re-recognition is an important research because it affects applications such as intelligent monitoring, content-based video retrieval, and human-computer interaction. It can help relay tracking and criminal suspect detection in large-scale video surveillance systems. Although the existing traditional pedestrian re-recognition methods have been widely applied to address practical problems, they have deficiencies such as low recognition accuracy, inefficient computation, and difficulty to adapt to specific applications. In recent years, the pedestrian re-recognition algorithms based on deep learning have been widely used in the pedestrian re-recognition field because of their strong adaptive ability and high recognition accuracy. The deep learning models provide a technical approach for pedestrian re-recognition tasks with their powerful learning ability. However, the pedestrian re-recognition method based on deep learning also has the following problems: First, the existing deep learning pedestrian re-recognition methods lack memory and prediction mechanisms, and the deep learning methods offer only limited improvement to pedestrian re-recognition accuracy. Second, they exhibit overfitting problems. Finally, initializing the existing LSTM parameters is problematic. In view of this, this paper introduces a revertive connection into the pedestrian re-recognition detector, making it more similar to the human cognitive process by converting a single image into an image sequence; then, the memory image sequence pattern reidentifies the pedestrian image. This approach endows deep learning-based pedestrian re-recognition algorithms with the ability to memorize image sequence patterns and allows them to reidentify pedestrians in images. At the same time, this paper proposes a selective dropout method for shallow learning. Selective dropout uses the classifier obtained through shallow learning to modify the probability that a node weight in the hidden layer is set to 0, thereby eliminating the overfitting phenomenon of the deep learning model. Therefore, this paper also proposes a greedy layer-by-layer pretraining algorithm for initializing LSTM and obtains better generalization performance. Based on the above explanation, this paper proposes a pedestrian re-recognition algorithm based on an optimized LSTM deep learning-sequence memory learning model. Experiments show that the pedestrian re-recognition method proposed in this paper not only has strong self-adaptive ability but also identifies the average accuracy. The proposed method also demonstrates a significant improvement compared with other mainstream methods because it can better memorize and learn the continuous motion of pedestrians and effectively avoid overfitting and parameter initialization in the deep learning model. This proposal provides a technical method and approach for adaptive pedestrian re-recognition algorithms.

scholarly journals Discrete Deep Learning Based Collaborative Filtering Approach for Cold Start Problem

2019 ◽  
Vol 12 (3) ◽  
pp. 68-75
Author(s):  
Archana Kalidindi ◽  
◽  
Prasanthi Yavanamandha ◽  
Anusha Kunuku ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Collaborative Filtering ◽  
Cold Start ◽  
Cold Start Problem ◽  
Filtering Approach
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