Recommending music based on a user’s music preference is a way to improve user listening experience. Finding the correlation between the user data (e.g., location, time of the day, music listening history, emotion, etc.) and the music is a challenging task. In this paper, we propose an emotion-aware personalized music recommendation system (EPMRS) to extract the correlation between the user data and the music. To achieve this correlation, we combine the outputs of two approaches: the deep convolutional neural networks (DCNN) approach and the weighted feature extraction (WFE) approach. The DCNN approach is used to extract the latent features from music data (e.g., audio signals and corresponding metadata) for classification. In the WFE approach, we generate the implicit user rating for music to extract the correlation between the user data and the music data. In the WFE approach, we use the term-frequency and inverse document frequency (TF-IDF) approach to generate the implicit user ratings for the music. Later, the EPMRS recommends songs to the user based on calculated implicit user rating for the music. We use the million songs dataset (MSD) to train the EPMRS. For performance comparison, we take the content similarity music recommendation system (CSMRS) as well as the personalized music recommendation system based on electroencephalography feedback (PMRSE) as the baseline systems. Experimental results show that the EPMRS produces better accuracy of music recommendations than the CSMRS and the PMRSE. Moreover, we build the Android and iOS APPs to get realistic data of user experience on the EPMRS. The collected feedback from anonymous users also show that the EPMRS sufficiently reflect their preference on music.

The study addresses the limitations of traditional centrality measures in complex networks, especially in disease-spreading situations, due to their inability to fully grasp the intricate connection between a node’s functional importance and structural attributes. To tackle this issue, the research introduces an innovative framework that employs machine learning techniques to evaluate the significance of nodes in transmission scenarios. This framework incorporates various centrality measures like degree, clustering coefficient, Katz, local relative change in average clustering coefficient, average Katz, and average degree (LRACC, LRAK, and LRAD) to create a feature vector for each node. These methods capture diverse topological structures of nodes and incorporate the infection rate, a critical factor in understanding propagation scenarios. To establish accurate labels for node significance, propagation tests are simulated using epidemic models (SIR and Independent Cascade models). Machine learning methods are employed to capture the complex relationship between a node’s true spreadability and infection rate. The performance of the machine learning model is compared to traditional centrality methods in two scenarios. In the first scenario, training and testing data are sourced from the same network, highlighting the superior accuracy of the machine learning approach. In the second scenario, training data from one network and testing data from another are used, where LRACC, LRAK, and LRAD outperform the machine learning methods.

In this paper, we investigate an attention function combined with the gated recurrent units (GRUs), named as GRUA, to raise the accuracy of the customer preference prediction. The attention function extracts the important product features by using the time-bias parameter and the term frequency-inverse document frequency (TF-IDF) parameter for recommending products to a customer in the ongoing session. We show that the attention function with the GRUs can learn the customer’s intention in the ongoing session more precisely than the existing session based recommendation (SBR) methods. Experimental results show that the GRUA outperforms two SBR methods, the stacked denoising autoencoders with collaborative filtering (SDAE/CF) and the GRUs with collaborative filtering (GRU/CF) based on the precision and recall evaluation metrics. The data from three publicly available datasets, the Amazon product review dataset, the Xing dataset, and the Yoo-choose click dataset, are used to evaluate the performance of the GRUA with the SDAE/CF and the GRU/CF. The paper shows that adopting the attention function into the GRUs can dramatically increase the accuracy of the product recommendation in the SBR.

Multilingual multimodal (MM) summarization, involving the processing of multimodal input (MI) data across multiple languages to generate corresponding multimodal summaries (MS) using a single model, has been under explored. MI data consists of text and associated images, while MS incorporates text alongside relevant images aligned with the MI context. In this paper, we propose an MM summarization model by fine-tuning transformers (MMSFT), focusing on low-resource languages (LRLs) such as the Indian languages. MMSFT comprises multilingual learning for encoder training, incorporating multilingual attention with a forget gate mechanism, followed by MS generation using a decoder. In the proposed approach, we use publicly available multilingual multimodal summarization dataset (M3LS). Evaluation utilizing ROUGE metrics and the language-agnostic target summary metric (LaTM) illustrates MMSFT’s significant enhancement over existing MM summarization models like mT5 and VG-mT5. Furthermore, MMSFT yields better or equivalent summaries compared to existing MM summarization models trained separately for each language. Human and statistical evaluation reveal MMSFT’s significant improvement over existing models, with a p-value ≤ 0.05 in paired t-tests.