Cross-domain recommendation is the central problem of personalized systems, especially in those cases where user prediction needs to be promoted across different kinds of content (e.g., music to books, or videos to articles). Typical collaborative filtering models cannot work well in this environment because user behavior representations are usually inconsistent in different domains and most existing neural models have a problem of overfitting on individual domains and lack generalization. This paper presents a new self-supervised dual-LLM model of getting user embeddings to transfer across domains through multi-view representations. In particular, two domain-specificities LLMs are trained simultaneously, sharing layers of user embedding. Such embeddings are considered to be cross-modal semantic anchors, which are learned through a joint contrastive loss (where semantically similar user profiles in a domain are brought closer to each other) and a reconstruction loss (where intra-domain similarity should be maintained). This combined goal allows this model to map high-dimensional user embeddings in domain $A$ and $B$ into a common latent space so that in the target domains where user history may be sparse. It takes an advantage of both Masked Language Modelling (MLM), and next-item prediction tasks to retain modality-specific learning in both LLMs, and also use contrastive learning enforcement of alignment using positive pairs sampled based on implicit behavioral cues. On cross-domain benchmarks, the experimental results indicate an improved top-k accuracy, embedding similarity (via CKA and t-SNE distance) and across transfer tasks compared to current image matching approaches CDAN, DGRec and CoNet.
Вікторія Олександрівна Гіголаєва-Юрченко, Олена Юріївна Смирна, Лариса Віталіївна Давидович
У статті представлено комплексну характеристику ґендерних відмінностей у процесі навчання академічного вокалу, які розглядаються як суттєвий чинник формування індивідуалізованої педагогічної стратегії. На основі узагальнення власного практичного досвіду викладачами-вокалістами кафедри хорового диригування та академічного співу Харківської державної академії культури здійснено ґрунтовний аналіз анатомо-фізіологічних і психофізіологічних особливостей чоловічого й жіночого голосового апарату, що безпосередньо впливають на характер звукоутворення, розвиток регістрової структури, дихальну координацію та специфіку резонансної підтримки звуку. Авторами окреслено специфіку методичної роботи з представниками різної статі: зокрема, для жіночих голосів важливим є формування стійкої опори в нижньому регістрі, вирівнювання регістрових переходів, розвиток гнучкості звуковедення; для чоловічих – подолання напруги у верхньому регістрі, формування м’якої атаки звуку та стабілізація позиції в перехідній зоні. Практика педагогів кафедри ХДАК підтверджує ефективність індивідуального підходу з урахуванням не лише морфологічних, а й емоційно-поведінкових параметрів кожного студента / студентки.Фахівцями проаналізовано відмінності у психоакустичному сприйнятті власного голосу студентами різної статі, що суттєво впливає на динаміку розвитку інтонаційної точності, тембрової стабільності та сценічної впевненості. У педагогічному процесі викладачами активно використовуються методичні прийоми, апробовані в Харківській державній академії культури, зокрема варіативне застосування вокалізів, робота над змішаними регістрами, поетапне ускладнення репертуару залежно від темпів психофізіологічної адаптації студента.У статті бґрунтовано важливість добору навчального матеріалу не лише за параметрами голосового типу (тенор, сопрано тощо), а й відповідно до емоційної чутливості, рівня сценічної готовності та індивідуального потенціалу самовираження.У підсумку наголошено на центральній ролі гнучкої, ґендерно-орієнтованої педагогічної моделі, яка реалізується викладачами кафедри хорового диригування та академічного співу ХДАК, і базується на глибокому розумінні взаємозв’язку фізіологічних, психофізіологічних та психологічних чинників вокального розвитку. Визначено перспективи подальших досліджень у напрямі ґендерно-орієнтованої вокальної педагогіки, зокрема в контексті професійної підготовки академічних співаків у мистецьких освітніх закладах.
Stress is a widespread issue that tells Mental and physical health, look good Cognitive and functional solutions. This is a job that is done He introduces a system based on deep learning for meditation Insights from facial expressions, pairs Personalized stress reduction tips. Using a FER dataset optimized for stress-specific sensitivity classification, the system allows the user to upload an image or log in in real time. They investigate through their laptop cameras. It has been worked on before Inputs are analyzed using a trained set model by combining Xception with NasNetMobile construction, which made it most accurate In classifying stress and nonstress situations. above By detecting stress, the system provides customized results Suggestions, like movies, music, websites Games and books. Performance descriptions such as Accuracy, recall, and F1 scores were assessed determine the effectiveness of the policies. There are consequences It is determined by the forward-backward correlative interaction,. Make sure they are usable and engaged. This is the whole point. The method enhances users' well-being by simplifying it timely and relieving stress detection.
Joel Osei-Asiamah, M. Alazzam, Ajmeera Kiran
et al.
Heterogeneous big data streams are used to engineer an effective cross-domain transfer learning framework for personalized recommendation systems that effectively leverages. Existing recommendation models have challenges with dealing with data sparsity, weak domain adaptability, and a lack of the capability to handle multi-format real-time data, thus not performing as effectively in dynamic environments. The suggested DANN-CF framework integrates Domain-Adversarial Neural Networks (DANN) and Neural Collaborative Filtering (NCF) to allow the model to learn domain-invariant user tastes from diverse data sources such as ratings, reviews, and item features. The Douban Dataset (Ratings, Reviews, Side Information) validates the system's performance across different domains such as movies, books, and music. Implemented on the Python platform with Apache Flink and TensorFlow on simulated data streams, DANN-CF improves precision while promoting scalability and flexibility. It greatly enhances recommendation precision over traditional single-domain models with an RMSE value of 0.1687, personalized content presentation, and smart education through accurate, real-time, cross-context recommendations.
This paper presents EDTE: Dynamic Topic-Sentiment Cross-Attentive Fusion for Movie Recommendation, an end-to-end framework that integrates contextual topic modeling (CTM) and hierarchical sentiment analysis, combined with cross-attention and temporal attention to capture preference drift. EDTE encodes reviews into contextual topics and multi-granularity sentiment, aligns the two via a Transformer-based cross-attentive module, and weights historical interactions with recency- and relevance-aware temporal attention. On the Douban dataset (280 K users, 12,147 movies, 1.85 M reviews), EDTE achieves precision 0.768, recall 0.751, F1 0.759, and diversity 0.642; compared with TextCNN, LSTM, BERT4Rec, and CF, it shows consistent gains. Ablation indicates the topic and sentiment components contribute 0.4% and 0.3%, respectively; cross-domain evaluations on books and music show modest drops ($3.4 \%, 4.9 \%$); inference remains under 100 ms with near-linear scaling. Overall, the evidence suggests EDTE is effective and practically deployable for large-scale sequential movie recommendation.
The widespread availability of pseudonymized user datasets has enabled personalized recommendation systems. However, recent studies have shown that users can be de-anonymized by exploiting the uniqueness of their data patterns, raising significant privacy concerns. This paper presents a novel approach that tackles the challenging task of linking user identities across multiple rating datasets from diverse domains, such as movies, books, and music, by leveraging the consistency of users’ rating patterns as high-dimensional quasi-identifiers. The proposed method combines probabilistic record linkage techniques with quasi-identifier attacks, employing the Fellegi–Sunter model to compute the likelihood of two records referring to the same user based on the similarity of their rating vectors. Through extensive experiments on three publicly available rating datasets, we demonstrate the effectiveness of the proposed approach in achieving high precision and recall in cross-dataset de-anonymization tasks, outperforming existing techniques, with F1-scores ranging from 0.72 to 0.79 for pairwise de-anonymization tasks. The novelty of this research lies in the unique integration of record linkage techniques with quasi-identifier attacks, enabling the effective exploitation of the uniqueness of rating patterns as high-dimensional quasi-identifiers to link user identities across diverse datasets, addressing a limitation of existing methodologies. We thoroughly investigate the impact of various factors, including similarity metrics, dataset combinations, data sparsity, and user demographics, on the de-anonymization performance. This work highlights the potential privacy risks associated with the release of anonymized user data across diverse contexts and underscores the critical need for stronger anonymization techniques and tailored privacy-preserving mechanisms for rating datasets and recommender systems.
Emotional well-being refers to the ability to manage one’s emotions, cope with stress, and maintain a positive outlook. EmoRoBERTa-base is an innovative platform designed to enhance emotional well-being in the digital age. By employing advanced sentiment analysis techniques, EmoRoBERTa-base accurately interprets user input to provide deep insights into their emotional states. The platform features Dr. Zen, an AI chatbot that offers personalized support, practical advice, and empathetic listening to help users navigate their emotions. Additionally, EmoRoBERTa-base provides tailored recommendations for books, movies, music, and yoga/meditation practices, ensuring that users receive content that resonates with their current emotional needs. With guided yoga and meditation techniques, the platform empowers users to manage and overcome negative emotions, fostering resilience and inner peace. Overall, EmoRoBERTa-base serves as a digital sanctuary, promoting holistic emotional well-being and self-awareness.
The self-regulation support strategies employed by Chinese kindergarten teachers and the challenges they face when implementing these strategies are investigated. Self-regulation is considered a crucial aspect of school readiness, and this study focuses on children aged 3-6 years. Through a qualitative approach that involves interviews with six kindergarten teachers, this study explores the different strategies used by teachers to promote self-regulation among young children. The findings show that Chinese kindergarten teachers use various techniques, including games, pretend play, music and songs and picture books, to help children develop their self-regulation skills. However, teachers may encounter challenges such as children not engaging in the activities and a lack of experience among the teachers themselves. In this study, the importance of effective self-regulation support strategies in early childhood education is highlighted, and insights are also provided into the specific approaches used by Chinese kindergarten teachers.
In the data-driven environment we live in today, where disorganization may inhibit productivity and workflow efficiency, effective digital file management is essential. This study and its suggested model present a novel mechanism concept a Python-based system created to address the widespread problem of disarrayed digital file systems. The “Group by” function of file managers available on many OSs currently does not address this problem because it groups files solely on the basis of one type of extension. Reducing the time & effort needed for manual organization, the suggested methodology automates the sorting & categorizing of files, including photos, documents, music, videos, archives, books, & code. Through its ability to optimize digital workflows & improve user experiences, this technology makes file management easier. To better understand the scalability, performance of the designed module, we conducted extensive performance testing on different devices & architectures. The results of this testing provided insightful information that can be applied to improve digital file organization in the real world. Additionally, the information revealed factors & relationships related to the organizing & sorting at the hardware level.
Władysławowi Leszczyńskiemu (1616–80), kapelmistrzowi zespołu jasnogórskiego, przypisywano dotychczas dwie zachowane w Archiwum Krakowskiej Kapituły Katedralnej msze: Missa per octavas i Missa cum Credo per octavas. Jak wykazano w niniejszym artykule, drugi z utworów w rzeczywistości nie jest jego dziełem, a zmienioną wersją Missae pro Nativitate Domini Nostri Jesu Christi zachowanej również w zbiorach wawelskich, sygnowanej inicjałami „M.M.” i przypisywanej Marcinowi Mielczewskiemu. Wskazano ponadto kolejny, nieznany wcześniej przekaz tej mszy i poddano refleksji kwestię jej atrybucji.
Harald Sæverud (1897–1992) var hele sitt liv en omdiskutert komponist, men ikke desto mindre en av Norges fremste. I denne artikkelen belyses hans siste store verk, nemlig Keiser og Galilæer (1986) for orkester og kor. Ettersom det bygger på Ibsens skuespill av samme navn, kastes det dessuten lys over musikkens rolle i Ibsens dramatikk. I motsetning til Ibsens mer populære skuespill er Kejser og Galilæer et anliggende for spesielt interesserte. Den som ikke har et visst kjennskap til stykket, forstår lite av Sæveruds musikk. Artikkelen inkluderer derfor en gjennomgang av handlingsforløpet med fokus på tekstens musikalske muligheter. I artikkelen blir det vist hvordan skuespillets bærende idé, kampen mellom kristendom og hedendom, uttrykkes gjennom klare musikalske kontraster. Det antydes videre hvordan Keiser og Galilæer avspeiler ulike sider ved Sæveruds eiendommelige, kunstneriske personlighet, og at verket av denne grunn kan betraktes som kronen på komponistens livsverk.
V. S. Devi, Ashwanth Thimmareddy, Manoj Reddy Kancharakuntla
et al.
A movie recommendation system is an application which filters or predicts user preferences according to their selections. Several different items, like music, movies, books, news, research articles, uses recommendation systems. Its goal is to identify patterns in a dataset in order to deliver the most relevant data to a user dataset. Using a content-based filtering algorithm and the Python programming language, and can create this application. To accomplish the recommendations, content-based filtering looks for similarities between users and items. A data set comprising the user ID, ratings, item number, and time spent is included. Match user ID and ratings with these data using a mapping technique and correlation concept. The user's rating of the films they've watched should determine the next movie recommendation.
Nowadays, recommendation systems have revolutionized how we discover interests, employing an information filtering approach to predict user preferences. Typical application areas encompass books, news, music, videos, and movies. This paper introduces a movie recommendation system designed using the Alternating Least Squares (ALS) algorithm, a widely adopted collaborative filtering technique implemented in the PySpark ML library. The system is specifically tailored to handle large-scale datasets, making it well-suited for real-world applications. The core functionality of the system involves processing a dataset containing user ratings for movies. By applying the ALS algorithm, latent factors are learned for both users and movies. These latent factors acquire the essential likings of users with the intrinsic characteristics of movies. Leveraging these latent factors, the system predicts ratings, with an accuracy of 72.91%, that users would assign to unrated movies, thereby generating personalized movie recommendations.
A Recommendation System (also known as Recommender System) is built for the purpose of recommending similar items to the user based on their interest. The item can be anything like movies, music, books, songs, etc. Many famous platforms such as Netflix and YouTube also use recommendation system. This paper focuses on comparing and studying the performance of the Recommender System for books that uses various different techniques such as k-means Clustering, PCA, Feature Selection, etc.
Gesmond George Manuval, Thomas T George, Bilha P Aby
et al.
Online websites that recommend books, music, movies, and other media are becoming increasingly prevalent because of collaborative filtering. This online websites are using many algorithms to provide the better recommendation to attract the customer. Bayesian statistics, which is based on Bayes' theorem, is a technique for data analysis in which observable data are used to update the parameters of a statistical model. To discuss a strategy called item-based collaborative filtering, which bases predictions on the similarities between the said objects. This uses Machine Learning based Candidate Recommendation System which uses Bayesian Model database to assess the proposed method. The actual results show that for collaborative filtering which is based on correlation, the Bayesian techniques we have proposed outperform traditional algorithms. Also discuss a technique for improving prediction accuracy that combines the Simple Bayesian Classifier with user- and item-based collaborative filtering. The user-based recommendation is then applied to the matrix once the user-item rating matrix has been filled out with pseudo-scores produced by the item-based filter. This model is demonstrated that the combined approach outperforms the individual collaborative recommendation approach. The creation of UI based web application will help Students to manage achievement details. Job seekers and admin will be given a separately formatted version of the application where, students can upload and view their certificate, wherein admin can access student achievement details categorized by different parameters. This proposed model is developed under the service learning scheme to benefit both job seeker and recruiter.
This work explores unifying knowledge enhanced recommendation with multi-domain recommendation systems in a conversational AI assistant application. Multi-domain recommendation leverages users’ interactions in previous domains to improve recommendations in a new one. Knowledge graph enhancement seeks to use external knowledge graphs to improve recommendations within a single domain. Both research threads incorporate related information to improve the recommendation task. We propose to unify these approaches: using information from interactions in other domains as well as external knowledge graphs to make predictions in a new domain that would not be possible with either information source alone. We develop a new model and demonstrate the additive benefit of these approaches on a dataset derived from millions of users’ queries for content across three domains (videos, music, and books) in a live virtual assistant application. We demonstrate significant improvement on overall recommendations as well as on recommendations for new users of a domain.
The proposed method utilizes emotional interaction capabilities to boost the user's emotions. The proposed recommender application would serve as a personal assistant for analysing the user's emotions. The process of human emotion prediction on the facial expressions of the user. With the eye sensor, the program may identify the user's emotions (such as happy, sad, fear, disgust, surprise, and anger). Furthermore, the integrated Artificial Intelligence (AI) based algorithm intends to transform the user's emotions from negative to positive by providing suitable recommendations. Since media has a strong effect on one's emotions in today's world, the proposed model recommends movies, music, and books to improve users' emotions. Depending on the user's emotions, various activities such as watching movies, listening to songs, and reading books will be recommended.
Compiled by Kai WestThe books listed in this column address music as it relates to political expression or focus on power relationships between individual musicians or musical communities and a governing authority. Readers are welcome to submit additional titles to musicandpolitics@umich.edu for possible inclusion in the next issue.
Music is universally important to every human being. It relates to, and is part and parcel of
children’s education where creative experiences and manipulative skills are gained and
developed. Every child responds to the rhythm and the activities of man are often connected with
music. Through musical activities, children acquire the basic skills, attitudes, values, norms,
customs, and knowledge and tend to appreciate of their culture. Like every other human being,
the handicapped children too, possess aptitudes, skills, concepts and knowledge that if
appropriately utilized will help to solve many of the physical, social, educational, political and
economic problems in the society. The handicapped children can learn how to sing, dance, play
musical instruments and perform other musical skills in order to demonstrate their capabilities
and relevance in the society. This paper therefore, examines the basic educational needs and
musical activities of the handicapped children and sees how best music education can effectively
be used to enhance livelihood of this vulnerable group.