Content Recommendation System Research Articles

Video Summarization using Object Detection Method

Video summarization is a fundamental challenge in the field of computer vision and multimedia processing, aimed at condensing lengthy videos into concise representations without compromising the essential content and context. This project focuses on the integration of object detection techniques into the process of video summarization, harnessing the power of deep learning to automatically identify and extract key objects and events from video sequences. By leveraging state-of-the-art object detection models and innovative summarization algorithms, this project aims to enhance the efficiency and effectiveness of video summarization, enabling users to quickly grasp the content and significance of videos without the need for exhaustive playback. The proposed approach not only streamlines video browsing and content comprehension but also holds potential applications in various domains, including surveillance, video indexing, and content recommendation systems

Co-Vision: Automated Video Context Understanding and Classification Using AI

In today's digital landscape, a challenge is presented by the ever-growing volume of video content: how to efficiently understand, access, and utilize this wealth of information. This challenge is addressed through developing an innovative web application combining video understanding, content recommendation, and accessibility features. The web application's core is found in its ability to be automatically analyzed and comprehended by videos' content. Valuable insights are extracted, key events, objects, and actions are identified, and textual explanations are generated, all of which are leveraged by state-of-the-art machine learning techniques in computer vision and natural language processing. The comprehension of video content forms the foundation for two significant applications, such as content recommendation, in which the content of videos and user preferences are understood, and our application revolutionizes content recommendation systems. Personalized video recommendations are provided, ensuring that content is discovered by users that precisely match their interests and needs. This user experience enhancement, along with resulting higher user engagement and content consumption on recommendation-driven platforms, is achieved. Accessibility is increased as the project promotes inclusivity by making video content more accessible to a broader audience. For individuals with disabilities, textual explanations and transcripts are generated by our application, breaking down barriers to understanding. Additionally, the convenience of summarizing lengthy videos is offered, enabling users to quickly grasp key insights without the need to watch the entire content. This enhanced accessibility is particularly valuable in educational contexts and beyond, where content consumption is made more efficient and equitable.

Enhancing Digital Learning Outcomes Through the Application of Artificial Intelligence: A Comprehensive Review

This research investigates the application of artificial intelligence (AI) in digital learning environments and its impact on learning outcomes. A comprehensive review of literature was conducted, encompassing studies from various educational levels and settings. The analysis reveals promising findings regarding the effectiveness of AI interventions, including intelligent tutoring systems, adaptive learning platforms, virtual assistants, and content recommendation systems, in enhancing learning outcomes. Learners exhibit high levels of engagement and satisfaction with AI-enhanced learning environments, appreciating the interactive features and personalized support provided by AI technologies. However, challenges and limitations associated with AI implementation, such as technical issues, privacy concerns, and ethical considerations, were identified. The research contributes valuable insights into the potential benefits and risks of AI in education, with implications for both research and practice. Future research directions include optimizing AI algorithms, exploring ethical and social implications, and addressing educator training needs to ensure the successful integration of AI technologies into teaching and learning processes.

HINDI TEXT CATEGORIZATION & TRANSLATION

The Hindi Text Translation and Categorization aims to develop a comprehensive system for translating and categorizing Hindi text content. This project addresses the growing need for effective language processing tools in the context of Hindi, one of the most widely spoken languages globally. The project consists of two primary components: translation and categorization. The translation module employs state-of-the-art machine translation techniques to provide accurate and contextually relevant translations between Hindi and other languages. This functionality is essential for breaking down language barriers and facilitating communication across diverse linguistic communities. The categorization module focuses on organizing and classifying Hindi text content into relevant categories or topics. Leveraging advanced natural language processing (NLP) algorithms, the system can analyze the semantic meaning of text and assign it to appropriate categories. This categorization enhances content management, information retrieval, and overall user experience, particularly in applications such as content recommendation systems and information filtering.

Topic Modelling of Web Pages with Latent Dirichlet Allocation Methods

Topic modelling with Latent Dirichlet Allocation (LDA) is a popular technique used in natural language processing to uncover hidden thematic structures within a collection of documents. When applied to web pages, LDA can help in identifying prevalent topics or themes across these pages.. This study delves into the utilization of Latent Dirichlet Allocation (LDA) methods to extract underlying topics within web pages, a fundamental pursuit in understanding the multifaceted landscape of online information. Web content analysis presents unique challenges owing to its diverse nature—comprising text, images, videos, and structured HTML elements—mandating rigorous preprocessing strategies to homogenize the data. By adapting the LDA model to accommodate these challenges, this research tackles the task of uncovering latent thematic structures prevalent across web content. Methodologically, the study explores parameter tuning and model adaptation to optimize LDA for web page analysis, navigating complexities such as varied content formats, noise, and inherent biases in web data. Addressing these intricacies involves parsing HTML, extracting meaningful textual information, and refining tokenization processes. Evaluating the fidelity and interpretability of discovered topics becomes pivotal, prompting the utilization of coherence scores, perplexity metrics, and human assessment to gauge the quality of generated topics. Additionally, this research confronts the dynamic nature of web content, proposing strategies like continuous model retraining and dynamic topic modeling to accommodate evolving trends and updates. Practical applications of the extracted topics span a spectrum of domains, encompassing content recommendation systems, user behavior analysis, sentiment analysis, targeted advertising, and the enhancement of search algorithms for improved relevance and user engagement. Supported by illustrative case studies, this study elucidates how LDA serves as a potent mechanism to distill coherent and meaningful topics from web pages, offering invaluable insights into the hidden structures within the vast expanse of online information. St This comprehensive abstract encapsulates the depth and breadth of employing LDA for the analysis of web content, encompassing challenges, methodologies, evaluations, applications, and real-world implications.

CMF-SMR: Convolutional matrix factorization for sequential movie recommendations

Recommender system (RS) has become an essential component of e-commerce, social media, and other online platforms. Collaborative filtering (CF) is one of the most commonly used techniques in RS that relies on user-item interactions to generate recommendations. However, CF suffers from the cold-start problem, sparsity, and scalability issues. To address these challenges, this work propose a hybrid system called Convolutional Matrix Factorization for Sequential Movie Recommendations (CMF-SMR), which combines matrix factorization (MF) with convolutional neural networks (CNNs). CMF-SMR leverages the non-linear feature extraction capabilities of CNNs and the representation learning abilities of deep learning to enhance the accuracy and robustness of traditional MF-based RS. Specifically, CNNs and MF were used to respectively extract features from user-item interaction data and use them as input for learning user and item representations. The learned representations are then used to predict user-item ratings. This work evaluates the performance of our proposed method on two publicly available datasets, and the experimental results demonstrate that our method outperforms several state-of-the-art techniques in terms of accuracy, scalability, and robustness. Moreover, this work conduct evaluation metrics to demonstrate the accuracy of our proposed method. Overall, our proposed CMF-SMR provides a promising solution for addressing the limitations of traditional CF-based RS and can be applied in various domains, including e-commerce, social media, and personalized content recommendation systems.

Numerical analysis-oriented Kruskal algorithm for the analysis and integration of effective components of university music pedagogy

Abstract This paper constructs a collection of effective teaching methods based on the improved K-Means clustering algorithm for clustering and dividing effective components of music teaching in colleges and universities. By analyzing the personalized content recommendation system, we can construct a recommendation system based on teaching content using information retrieval and filtering techniques. The collaborative filtering recommendation algorithm is used to ensure the accurate placement of teaching content. The Kruskal algorithm is used to find the minimum spanning tree of teaching effective components, and the K-means clustering principle is applied to the division of music teaching effective components, and the cluster of effective teaching components is divided by the clustering algorithm. According to the findings, mind-body integration and the teaching goal of valuing creativity were classified as effective teaching components in music. Personal aesthetics had a 0.6 influence on musical creativity, and a free environment had a 0.3 influence.

Effective News Text Summarization Techniques

n proposed work, we successfully implemented a news text summarization system using Natural Language Processing (NLP) techniques and the Latent Semantic Analysis (LSA) algorithm. The purpose of our project was to extract important information from a large volume of news articles and present it in a concise and easily understandable manner. To achieve this, we utilized the LSA algorithm, which is known for its ability to capture the underlying semantic structure of text. LSA employs a mathematical model to analyse relationships between words in a document, creating a semantic representation where words with similar contexts are grouped together in a vector space. The LSA-based summarization process involved several steps. First, we pre-processed the news articles by removing stop words, punctuation, and other non-relevant elements. Then, we constructed a term-document matrix, where rows represented words and columns represented documents, with matrix values representing word frequencies. Next, we applied Singular Value Decomposition (SVD) to the term-document matrix. SVD helped reduce the matrix's dimensionality by identifying the most important latent semantic concepts. This resulted in a lower-dimensional representation that captured the essential information. Finally, we identified the most important sentences in the news articles by measuring the cosine similarity between each sentence and the summary. Sentences with the highest cosine similarity scores were selected as summary sentences. The proposed system demonstrated the effectiveness of the LSA algorithm for news text summarization. By capturing the semantic structure of the text, it generated summaries that allowed users to understand the key points of a news article quickly and easily. Our implementation had practical applications for content recommendation systems, news aggregation platforms, and personalized news feeds. However, it is important to acknowledge the limitations of the LSA algorithm. It may struggle with handling idiomatic expressions and can be sensitive to the quality of the input data. These considerations highlight the need for ongoing research and development to enhance the performance and robustness of news text summarization systems.

A content recommendation system for e-learning using enhanced Harris Hawks Optimization, Cuckoo search and DSSM

The e-learning is necessary in this fast internet world, especially during this pandemic situation, to continue education without any interruption and it is used reduce the educational cost significantly when reduces the energy loss. Generally, machine learning and deep learning algorithms are used to identify patterns that facilitate learning and help learners understand concepts easily. Many content recommendation systems are available for assisting learners as e-learning applications by providing the required study materials. Despite the fact that existing recommendation systems struggle to provide precise content to e-learners due to the availability of a massive volume of data on the internet and other repositories. For this purpose, we propose a new content recommendation system for recommending suitable content to learners according to their interests and learning capabilities. The proposed content recommendation system employs a newly proposed semantic-aware hybrid feature optimizer that incorporates new optimization algorithms such as the Enhanced Personalized Best Cuckoo Search Algorithm (EpBestCSA) and the Enhanced Harris Hawks Optimization Algorithm (EHHOA) for selecting suitable features that aid in improving prediction accuracy, as well as a newly proposed Deep Semantic Structure Model (DSSM) that incorporates Artificial Neural Network (ANN) and Convolutional Neural Network (CNN). According to the experimental results, the proposed model outperforms other recommendation systems in terms of precision, recall, f-measure, and prediction accuracy. The ten-fold cross validation is done to test the performance of the proposed methodology.

Content Recommendation Systems in Web-Based Mental Health Care: Real-world Application and Formative Evaluation.

Recommender systems have great potential in mental health care to personalize self-guided content for patients, allowing them to supplement their mental health treatment in a scalable way. In this paper, we describe and evaluate 2 knowledge-based content recommendation systems as parts of Ginger, an on-demand mental health platform, to bolster engagement in self-guided mental health content. We developed two algorithms to provide content recommendations in the Ginger mental health smartphone app: (1) one that uses users' responses to app onboarding questions to recommend content cards and (2) one that uses the semantic similarity between the transcript of a coaching conversation and the description of content cards to make recommendations after every session. As a measure of success for these recommendation algorithms, we examined the relevance of content cards to users' conversations with their coach and completion rates of selected content within the app measured over 14,018 users. In a real-world setting, content consumed in the recommendations section (or "Explore" in the app) had the highest completion rates (3353/7871, 42.6%) compared to other sections of the app, which had an average completion rate of 37.35% (21,982/58,614; P<.001). Within the app's recommendations section, conversation-based content recommendations had 11.4% (1108/2364) higher completion rates per card than onboarding response-based recommendations (1712/4067; P=.003) and 26.1% higher than random recommendations (534/1440; P=.005). Studied via subject matter experts' annotations, conversation-based recommendations had a 16.1% higher relevance rate for the top 5 recommended cards, averaged across sessions of varying lengths, compared to a random control (110 conversational sessions).Finally, it was observed that both age and gender variables were sensitive to different recommendation methods, with responsiveness to personalized recommendations being higher if the users were older than 35 years or identified as male. Recommender systems can help scale and supplement digital mental health care with personalized content and self-care recommendations. Onboarding-based recommendations are ideal for "cold starting" the process of recommending content for new users and users that tend to use the app just for content but not for therapy or coaching. The conversation-based recommendation algorithm allows for dynamic recommendations based on information gathered during coaching sessions, which is a critical capability, given the changing nature of mental health needs during treatment. The proposed algorithms are just one step toward the direction of outcome-driven personalization in mental health. Our future work will involve a robust causal evaluation of these algorithms using randomized controlled trials, along with consumer feedback-driven improvement of these algorithms, to drive better clinical outcomes.

Intelligent personalized content recommendations based on neural networks

To effectively assist users in discovering content tailored to their specific interests, this research aims to create an intelligent content recommendation system. The inadequacy of conventional recommendation models, which depend uniquely on historical reading data, becomes evident in their limited capacity to meet contemporary users' diverse and ever-changing preferences within the information. The proposed architecture makes the most of the advancements in deep learning technology. It integrates the self-attention mechanism, allowing for precise calibration of the significance attributed to each feature within the news data. The proposed multilevel data classification network enables a more refined and personalized knowledge of users' preferences and the array of content information attributes while incorporating the users' unique characteristics. The proposed model achieved an accuracy rate of 85.2%, a recall rate of 83.7%, an F1 score of 84.3%, and an Area Under the Curve (AUC) of 84.5%. By developing a multilevel, intelligent, personalized content recommendation network, the research attempts to introduce a solution that effectively provides users' preferences, thereby enriching their experience in discovering relevant information within the modern digital system.

Sentiment-aware Content Recommendation using LSTM-based Collaborative Filtering

The need for this work arises from the ever-growing demand for more precise and efficient content recommendation systems. Existing methods, while serving their purpose, are not without limitations. They often struggle to capture the intricate nuances of user sentiment, which is crucial for personalized content recommendations. This limitation results in suboptimal precision, accuracy, recall, and speed, ultimately leading to less satisfying user experiences. To address these shortcomings, this paper introduces a novel approach, leveraging the fusion of Bidirectional Long Short-Term Memory (BiLSTM) with BERT, combined with a Graph Convolutional Network (GCN). This innovative model brings together the power of natural language processing and graph-based techniques, enhancing the efficiency of sentiment-aware content recommendation and filtering. The use of BiLSTM and BERT allows our model to grasp the contextual intricacies of user preferences, making it more adept at discerning sentiment in textual data samples. The incorporation of GCN further enriches our model's capabilities by leveraging the underlying connections between users and content, enabling it to provide more personalized recommendations. The advantages of our proposed approach become evident through rigorous testing on multiple contextual datasets. Compared to existing methods, our model exhibits notable improvements, with an 8.3% boost in precision, an 8.5% increase in accuracy, a 5.9% rise in recall, and a 4.5% enhancement in speed. Moreover, our model achieves a 7.5% better Area Under the Curve (AUC), solidifying its effectiveness in sentiment-aware content recommendation.&#x0D; In conclusion, this work fills a crucial gap in the realm of content recommendation by introducing a sophisticated model that excels in capturing user sentiment and delivering more precise and efficient recommendations. These advancements pave the way for improved user experiences and hold significant implications for a wide range of applications, from e-commerce to personalized content delivery platforms.

A Goal-driven Personalized Educational Content Recommendation System for Self-Learning

A Goal-driven Personalized Educational Content Recommendation System for Self-Learning

Towards an automated approach for smart sterility test examination.

As new technologies emerge, deep learning applications are often integral parts of new products as features and often as differentiating benefits. This is especially notable in commercial consumer products in everyday applications, such as voice assistants or streaming content recommendation systems. Due to the power and applicability of these deep learning technologies significant efforts are being directed to the development and integration of appropriate models into science and engineering applications to supplant analogue systems that may be highly prone to human error. Here we present an innovative, low-cost approach to advance sterility assessment workflows that are required and regulated within drug release/manufacturing processes. The model system leverages off-the-shelf hardware as well as deep learning models to detect and classify different microbial contaminations in test containers. The paired hardware and software tools were evaluated in experiments using common model organisms (C. sporogenes, P. aeruginosa, S. aureus). With this approach we were able to detect all three test organisms across 40 experiments, furthermore we were capable of classifying the present organisms with an average classification accuracy of over 87%.

How do Students Perceive Artificial Intelligence in YouTube Educational Videos Selection? A Case Study of Al Ain City

This research focused on the students' perceptions of the role of Artificial Intelligence in the educational content recommendation system by using the SEM- Artificial Neural Network approach. Data obtained from structured surveys indicated that Artificial Intelligence plays a significant role in the educational content recommendation process. Most relationships proposed in the conceptual model remained significant, with p values as strong as p&gt; 0.000. Further, the IPMA and ANN also revealed in-depth results. Thus, this research concludes that the video recommendation system is helping young students to search for and find the educational content they want. Besides, this system also provides them with a searching facility with fewer efforts, adding more value to YouTube as one of the most prominent digital platforms today.

Content and Other Resources Recommendations for Individuals with Intellectual Disability: A Review

In this review paper, we look into how a recommendation system can be adapted to and support people with intellectual disability (ID). We start by reviewing and comparing the main classes of techniques for general-purpose content recommendation. Then, centering on individuals with ID, we collect information on their special needs that may be relevant to or affected by content recommendation tasks. We review the few existing recommendation systems specifically designed or adapted to the needs of this population and finally, based on the reviewed literature sources, we catalog the traits that a future content recommendation system should have in order to respond well to the identified special needs. We hope this listing of desirable traits and future directions in our concluding sections will stimulate research towards opening the doors to the digital world for individuals with ID.

Bots influence opinion dynamics without direct human-bot interaction: the mediating role of recommender systems

Bots’ ability to influence public discourse is difficult to estimate. Recent studies found that hyperpartisan bots are unlikely to influence public opinion because bots often interact with already highly polarized users. However, previous studies focused on direct human-bot interactions (e.g., retweets, at-mentions, and likes). The present study suggests that political bots, zealots, and trolls may indirectly affect people’s views via a platform’s content recommendation system's mediating role, thus influencing opinions without direct human-bot interaction. Using an agent-based opinion dynamics simulation, we isolated the effect of a single bot—representing 1% of nodes in a network—on the opinion of rational Bayesian agents when a simple recommendation system mediates the agents’ content consumption. We compare this experimental condition with an identical baseline condition where such a bot is absent. Across conditions, we use the same random seed and a psychologically realistic Bayesian opinion update rule so that conditions remain identical except for the bot presence. Results show that, even with limited direct interactions, the mere presence of the bot is sufficient to shift the average population’s opinion. Virtually all nodes—not only nodes directly interacting with the bot—shifted towards more extreme opinions. Furthermore, the mere bot’s presence significantly affected the internal representation of the recommender system. Overall, these findings offer a proof of concept that bots and hyperpartisan accounts can influence population opinions not only by directly interacting with humans but also by secondary effects, such as shifting platforms’ recommendation engines’ internal representations. The mediating role of recommender systems creates indirect causal pathways of algorithmic opinion manipulation.

Reduction of Information Collection Cost for Inferring Brain Model Relations From Profile Information Using Machine Learning

A content recommendation system based on human brain activity has become a reality. However, the cost of collecting the information from people is problematic. This article proposes a scheme that resolves the tradeoff between the inference performance from a profile model to a brain model and the cost of collecting profile information. In the proposed scheme, a machine learning model infers the brain model from the profile model and a feature selection method is applied to reduce the cost, i.e., the number of questionnaire items, of collecting profile information. Since only the top questionnaire items with the highest importance scores are used, we can maintain the inference performance as high as possible while limiting the number of questionnaire items. We demonstrate the effectiveness of the proposed scheme with a performance evaluation using an experimentally obtained brain model and a profile model created from real profile information. The results over different experimental parameters, video lengths, and feature selection methods demonstrate that the proposed scheme successfully identifies the top questionnaire items that contribute most significantly to the inference of brain models.

The Construction of Accurate Recommendation Model of Learning Resources of Knowledge Graph under Deep Learning

With the rapid development of science and technology and the continuous progress of teaching, it is now flooded with rich learning resources. Massive learning resources provide learners with a good learning foundation. At the same time, learners want to be precise from many learning resources. Second, it becomes more and more difficult to quickly obtain the learning resources you want. Therefore, it is very important to accurately and quickly recommend learning resources to learners. During the last two decades, a large number of different types of recommendation systems were adopted that present the users with contents of their choice, such as videos, products, and educational content recommendation systems. The knowledge graph has been fully applied in this process. The application of deep learning in the recommendation systems has further enhanced their performance. This article proposes a learning resource accurate recommendation model based on the knowledge graph under deep learning. We build a recommendation system based on deep learning that is comprised of a learner knowledge representation (KR) model and a learning resource KR model. Information such as learner’s basic information, learning resource information, and other data is used by the recommendation engine to calculate the target learner’s score based on the learner KR and the learning resource KR and generate a recommendation list for the target learner. We use mean absolute error (MAE) as the evaluation indicator. The experimental results show that the proposed recommendation system achieves better results as compared to the traditional systems.

ALGORITHMIC SUPPORT OF THE WEB SERVICE RECOMMENDATION SYSTEM FOR LEARNING FOREIGN LANGUAGES

This work is devoted to the analysis of algorithmic support of multimedia content recommender systems and the development of a web service toincrease the efficiency of learning foreign languages using a recommender system that personalized the selection of educational content for the user.To form a list of necessary multimedia content, the main criteria of the recommender system were selected, the basic needs of users were identified,which the system should solve, since increasing the efficiency of learning a foreign language is achieved not only by choosing teaching methods, butalso by watching multimedia content, namely news, films, educational videos, clips, etc. Therefore, in order to form a list of the necessary multimediacontent, the main criteria of the recommender system were formed, the main needs of users were identified, which the system must solve. From theside of the method for implementing algorithmic support, various types of data filtering were considered, from modern technical methods to librariesto ensure the functionality of the system, and the algorithm based on hybrid filtering was chosen, in which known user ratings are used to predict thepreferences of another user. Functional requirements have been developed and a web service has been proposed that allows a comprehensive impact onuser learning when learning a foreign language, software implementation of which is made using Java Script, Python and additional libraries. Thisimplementation allows you to build a process for tracking changes in user requirements and transfer information to the database (DB) and, afteranalyzing the input data, change the proposed multimedia content to the user. In the course of further research, it is planned to conduct practicalexperiments, taking into account the specifics of certain methods of teaching foreign languages and the use of statistical data to assess the effectivenessof the algorithm of the proposed recommender system.