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The future of many modern technologies includes machine learning and deep learning methodologies. One of the prominent applications of these technologies is the recommender system. Due to the rapid growth of the songs in digital formats, the searching and managing of songs has become a great problem. In this study, the authors developed a recommend...
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Developing online educational platforms necessitates the incorporation of new intelligent procedures in order to improve long-term student experience. Presently, e-learning Recommender Systems rely on deep learning methods to recommend appropriate e-learning materials to the students based on their learner profiles. Fine-grained sentiment analysis...
![Figure 2. Architecture of the study [23].](publication/375046128/figure/fig1/AS:11431281201670525@1698504486316/Architecture-of-the-study-23_Q320.jpg)
Through the Internet, learners can access available information on e-learning platforms to facilitate their studies or to acquire new skills. However, finding the right information for their specific needs among the numerous available choices is a tedious task due to information overload. Recommender systems are a good solution to personalize e-lea...
In the era of big data, effective recommendation systems are essential for providing users with personalized content and reducing search time on online platforms. Traditional collaborative filtering (CF) methods face challenges like data sparsity and the new-user or cold-start issue, primarily due to their reliance on limited user–item interactions...
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Citations
... Literature [24] improves the traditional collaborative filtering recommendation algorithm based on the latent factor model and proposes a music recommendation algorithm that combines clustering and latent factors. Literature [25] combined machine learning algorithms and deep learning algorithms to develop a personalized music recommendation system by combining both song popularity and rhythmic content through collaborative filtering algorithms. Literature [26] proposed an emotion-aware music recommendation algorithm based on a deep neural network (emoMR) using music emotion as a discrete representation, which utilized low-level audio features and music metadata to model and predict the user's music emotion and music preference in a continuous form. ...
The current music recommendation is less efficient and cannot meet the needs of most people. This paper combines deep learning and migration learning to research music recommendations. It mainly utilizes the hybrid recommendation method of label recommendation and noise reduction autoencoder to achieve accurate music recommendations after extracting the features of music and the user’s preference features. In addition, in order to explore the recommendation effect of the model in this paper, the deviation between the measured score and the actual score of this model is compared and analyzed as well as the similarity difference of music with different features is explored in terms of HR, Recall, and NDCG index performance. The results show that the number of songs with a deviation of 2 or less in the predicted scores of this recommender system all account for 88% of the total number of songs, which is better than other models. In terms of HR, Recall, and NDCG, this paper’s model has better recommendation performance than other models, and the similarity of music recommendation between the same album and different albums is more than 0.65. This study is of great significance for eliminating the information barriers to enhance and explore the value of music data information.
... The music recommendation system created by Garanayak, M. et al. [21] focuses primarily on content-based, collaborativebased, and popularity-based filtering algorithms while also proposing a hybrid method that combines these three filtering types. ...
INTRODUCTION: A thorough and timely investigation of any health-related problem is essential for disease prevention and treatment. The normal way of diagnosis may not be sufficient in the event of a serious illness problem. OBJECTIVE: Creating a medical diagnosis prototype that uses many machine learning processes to forecast any illness relied on symptoms explained by patients can lead to an errorless diagnosis as compared to the traditional ways. METHODS: We created a disease prediction prototype using ML techniques such as random forest, CART, multinomial linear regression, and KNN. The data set utilized for processing contained over 132 illnesses. Diagnosis algorithm outcomes the ailment that the person may be suffering from relied on the symptoms provided by the patients. RESULTS: When compared to CART and random forest (accuracy is 97.72%, multinomial linear regression and KNN produced the best outcomes. The accuracy of the KNN prediction and multinomial linear regression techniques was 98.76%. CONCLUSION: The diagnostic prototype can function as a doctor in the early detection of an illness, ensuring that medical care can begin in an appropriate time and many lives can be secured.
This research paper presents a data-driven analysis of anime preferences using the MyAnimeList (MAL) API. The objective of this study is to uncover valuable insights into the preferences of anime viewers by analyzing data obtained through MAL API. The paper begins with a comprehensive background that includes a detailed literature survey to understand anime recommendation systems. The methodology employed in this study involves utilizing the MAL API to collect and analyze data related to anime. The API provides access to a wide range of anime related information including ratings, ranks, genres etc. The collected data is then preprocessed to ensure its quality and suitability for analysis, involving data cleaning and transformation. Various data analysis techniques are applied to the preprocessed data. These techniques help identify patterns and trends in different fields such as genres, studios, media type etc. The findings from the data analysis provide valuable insights into the preferences of anime viewers and help to identify features that may be crucial while building the model. Then using cosine similarity model, content-based anime recommendation system is built after feature engineering. The results of this research highlight the importance of leveraging data-driven approaches in understanding and catering to the diverse preferences of anime viewers. By EDA uncovering patterns and trends in anime preferences, anime recommendation systems can be enhanced to provide more relevant recommendations to viewers. The implications of this research extend to the anime industry, where a deeper understanding of viewer preferences can inform content production and distribution strategies. In conclusion, this research paper presents a data-driven analysis and content-based recommendation system for anime using the MAL API and data science. The methodology employed involves data collection, preprocessing, Exploratory Data Analysis(EDA), Feature engineering and Model building. The findings contribute to enhancing anime recommendation systems and understanding the diverse preferences of anime viewers.
