Personalized Content Recommendation Research Articles

Impact of AI on Communication Relationship and Social Dynamics: A qualitative Approach

The integration of artificial intelligence (AI) into social media platforms has ushered in a new era of digital communication, offering unprecedented opportunities for content curation, relationship-building, and information exchange. Through this qualitative study, we have explored the multifaceted impact of AI-driven algorithms and natural language processing (NLP) technologies on user experiences and societal dynamics, addressing key research objectives and questions. Our findings underscore the transformative potential of AI in enhancing content curation and user interaction on social media platforms. Participants highlighted the benefits of personalized content recommendations and AI-enabled features such as chatbots, which streamline user interactions and provide instant support. Furthermore, AI algorithms play a crucial role in facilitating relationship-building through friend suggestions and group recommendations, fostering community engagement and social connections among users. However, alongside these benefits, our study also revealed significant challenges and ethical concerns associated with AI integration in social media. Participants expressed concerns about the proliferation of echo chambers and misinformation, fueled by algorithmic biases and the spread of false information through social bots. Privacy considerations emerged as a prominent issue, with participants emphasizing the need for transparency and accountability in AI implementation to safeguard user data and mitigate risks of algorithmic surveillance. In light of these findings, it is evident that the responsible deployment of AI technologies is paramount in ensuring positive user experiences and preserving the integrity of digital information ecosystems. Ethical considerations must guide the design and implementation of AI-driven algorithms, prioritizing transparency, fairness, and user empowerment. Platform operators, policymakers, and civil society stakeholders must collaborate to develop robust regulatory frameworks and governance mechanisms that uphold ethical standards and protect user rights in the digital age.

An Evaluation of Machine Learning Algorithms Used for Recommender Systems in Streaming Services

With the exponential growth of streaming services, the demand for effective recommender systems has become paramount. Recommender systems, powered by machine learning algorithms, play a critical role in analyzing user behavior and preferences to deliver personalized content recommendations. However, the abundance of available algorithms poses a challenge for streaming platforms in determining the most suitable approach for their recommender systems. This research paper presents an evaluation of various machine learning algorithms used in recommender systems for streaming services. Through a comparative analysis of collaborative filtering, content-based filtering, matrix factorization techniques, deep learning algorithms, and emerging approaches such as reinforcement learning and hybrid models, this study aims to provide insights into the strengths, limitations, and performance of each approach. By assessing the effectiveness of these algorithms, exploring their applications, and discussing recent advancements, this research contributes to the advancement of recommendation technology in streaming services, ultimately enhancing user satisfaction and engagement.

Increasing the Effectiveness of Prediction in Recommendation Engines Based on Collaborative Filtering

In the era of information abundance, the demand for personalized content recommendations has become paramount. Recommendation engines, particularly those employing collaborative filtering, play a pivotal role in delivering tailored suggestions based on user preferences. As technology evolves, the need to enhance the effectiveness of prediction algorithms within these engines becomes increasingly crucial. This research endeavors to contribute to this evolving landscape by delving into collaborative filtering methodologies, identifying challenges, and proposing novel strategies to elevate the accuracy and relevance of predictions in recommendation systems. Through this exploration, we aim to not only refine existing models but also pave the way for more sophisticated and reliable personalized content recommendations.
 This research aims to enhance prediction accuracy in recommendation engines utilizing collaborative filtering. Through an in-depth exploration of collaborative filtering techniques, we propose innovative approaches to improve the effectiveness of predictions. Our study addresses key challenges in collaborative filtering models, offering insights into refined algorithms and methodologies. By fine-tuning the collaborative filtering process, we anticipate a substantial boost in the overall performance of recommendation engines, ultimately advancing the field of personalized content suggestion. The simulation is performed using Java language and using two datasets Movie Lens 1M and Movie Lens 100K.The proposed model was evaluated using the Mean Absolute Error, Precision, and Recall.
 The proposed model achieved a mean absolute error value ranging between 0.78 and 0.84 using the Movie Lens 100K dataset, and a mean absolute error value ranging between 0.72 and 0.74 using the Movie Lens 1M dataset for different values of the number of user groups. As for precision and recall, the precision of the proposed model ranged between 0.97 and 0.985 using the Movie Lens 100K data set, and a precision value ranging between 0.944 and 0.954 using the Movie Lens 1M data set, also for different values of the number of user groups.
 As for the recall results, the proposed model achieved a recall value ranging between 0.755 and 0.85 using the Movie Lens 100K dataset, and a recall value ranging between 0.72 and 0.75 using the Movie Lens 100K dataset, also for different values of the number of user groups. These results were compared with the PMF, HPF, and NMF algorithms, where the proposed model proved its clear superiority over these algorithms. Using this analysis of the matrix allows us to obtain a good prediction accuracy of users' preferences and to find common groups of people with similar preferences.

Digital Content Creation: An Analysis of the Impact of Recommendation Systems

The success of digital content platforms, such as YouTube, relies on both the creativity of independent content creators and the efficiency of content distribution. By sharing advertising revenue with content creators, these platforms can motivate creators to exert greater effort. Most platforms use recommendation systems to deliver personalized content recommendations to each consumer. As creators’ revenues are contingent on their demand, the demand allocation criteria inherent in the recommendation system can influence their content creation behavior. In this paper, we investigate the influence of a platform’s recommendation system on revenue-sharing plans, content creation, profits, and welfare. Our results show that a platform could benefit by biasing recommendations, that is, recommending content that is not an ideal match to a consumer’s preference, to incentivize creators to produce better-quality content. We refer to this as a biased recommendation strategy. Interestingly, we find that such a biased recommendation strategy may lead to a win-win in which the platform, consumers, and content creators can benefit. Our study also shows that consumers may be worse off when they are more knowledgeable and less dependent on the recommendation system. In addition, the platform, consumers, and creators can benefit when the platform has more accurate information on consumer preferences. This paper was accepted by Raphael Thomadsen, marketing. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.03655 .

The comprehensive investigation of the role related to artificial intelligence in education

This systematic literature review extensively explores the prospects of Artificial Intelligence (AI) applications in the realm of learning from 2019 to 2023. It delves into key areas such as intelligent tutoring systems and online learning platforms, while meticulously examining their underlying technologies. The coverage includes methods for integrating AI into student models, as well as algorithms for enhancing adaptive learning and improving the precision of personalized learning content recommendations. The paper also discusses challenges and prospects, including the potential of AI to reshape the teaching profession and privacy concerns. The review underscores the ongoing necessity of striking a balance between human involvement and automation, emphasizing that AI serves as an auxiliary tool rather than a replacement in education. Additionally, the study investigates the integration of AI in online learning, emphasizing the need for user-friendly interfaces and robust data protection policies. Lastly, the research highlights the importance of interdisciplinary collaboration and ethical considerations, envisioning a future where AI and education seamlessly harmonize.

Web's Progression: Moving from Passive Content Consumption to Active Content Creation and Content Validation

This research paper reviews the evolution of the Web from its early phase, web 1.0, to the present state, web 3.0. The primary emphasis is placed on the shift that users have undergone, transitioning from content consumption to content creation, and now towards content validation. During the Web 1.0 era, users could only passively view static web pages. The introduction of Web 2.0 allowed users to generate and share content online. However, in the current era of Web 3.0, personalized content recommendations are offered through machine learning, which makes content validation more important. This study highlights the significance of these changes for digital media professionals, web developers, and web users, and concludes with recommendations for future research.

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.

Mobile Marketing: Exploring the Efficacy of User-Centric Strategies for Enhanced Consumer Engagement and Conversion Rates

This research investigated user-centric strategies in mobile marketing, focusing on consumer engagement and conversion rates. 400 questionnaires were distributed, with 385 usable responses obtained. The study used Partial Least Squares (PLS) analysis to assess the relationships between user-centric strategies and desired outcomes. The analysis yielded positive findings, confirming the effectiveness of user-centric strategies in driving consumer engagement and improving conversion rates in mobile marketing. Personalized content recommendations, interactive features, ease of use, location-based services, social integration, and push notifications were found to positively influence consumer behavior. The study emphasized the importance of personalization as a key driver of consumer engagement and conversion rates in mobile marketing. Tailored messages and experiences based on user preferences significantly enhanced consumer engagement. Additionally, interactive features and seamless user experiences fostered increased engagement and improved conversion rates.

Discuss the Social Pressure of Douyin on Female Hair Removal Behavior through Questionary

The research discusses the impact of Douyin, a popular social media, on the pressure of female hair removal behavior. It opens with an introduction to the Douyin platform and the characteristics of its pushing mechanism, including personalized content recommendations and homogenized content with big data. The author summarizes and analyzes the results of eighty questionnaires administered to female users of Douyin, taking into account the status of women as aesthetic objects in Chinese history and the evolution of beauty standards.

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.
 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.

ANALYSIS OF TECHNOLOGIES FOR PERSONALIZED CONTENT RECOMMENDATIONS ON THE INTERNET

The introduction of new technologies in the social information sector of the Internet is primarily due to increasing competition for the attention of users. In order to keep a person’s attention on your social network, you only need constantly to offer interesting, diverse content, and it should be different for different users. To solve these practical problems, technologies for personalized content recommendations are being developed. The relevance of the topic of this article is due to the widespread use on the Internet of personalized content recommendations and the emergence of publications (analytical materials, statistics), which address the shortcomings of these technologies. The purpose of the study is to analyze modern technologies of personalized content recommendations with the identification of positive and negative consequences of their practical application. The paper analyzes the algorithms of content ranking, which are used by various social networks, video hosting and other media resources. Theoretical substantiation of risks connected with creation of “information corridors” is given. The study found that these technologies could facilitate the process of users to search and consume information through recommendations only interesting content; personalization of advertising, facilitation of targeting; reducing the amount of harmful information or traumatic content. At the same time, these technologies lead to negative consequences: problems of censorship; information restrictions, or “information corridors”; influence on concentration and way of thinking. The presence of these problems does not allow calling the current technical achievements in the field under study, the ultimate. However, this applies to any technology that requires not only recognition of their benefits, but also careful analysis of shortcomings in order to timely correct them and establish the principles of responsible use of these technologies.

Personalized content recommendation in online health communities

PurposeRecommending suitable content for users of online health communities (OHCs) is critical for overcoming information overload problem and facilitate medical decision making, but remains not fully investigated. This study aims to provide a content recommendation approach to automatically match valuable health-related information for OHC members.Design/methodology/approachA framework of health-related content recommendation was proposed by leveraging rich social information in online communities. The authors constructed user influence relationship (UIR) utilizing users' interaction records, user profiles and user-generated content. The initial user rating matrix and the user post matching matrix were then created by analyzing text content of posts. Finally, the user rating matrix and the recommended content were generated for community members. Datasets were collected from an OHC to evaluate the effectiveness of the proposed approach.FindingsThe experimental results revealed that the proposed method statistically outperformed baseline models in content recommendation for users of OHCs.Research limitations/implicationsThe incorporation of social information can significantly enhance the performance of content recommendation in OHCs. The user post matching degree based on text analysis can improve the effectiveness of recommendation.Practical implicationsThis study potentially contributes to the social support exchange and medical decision making of community members and the sustainable prosperity of OHCs.Originality/valueThis study proposes a novel social content recommendation method for online health consumers based on UIRs by leveraging social information in OHCs. The results indicate the significance of social information in content recommendation of healthcare social media.

Harnessing Machine Learning to Personalize Web-Based Health Care Content.

Web-based health care content has emerged as a primary source for patients to access health information without direct guidance from health care providers. The benefit of this approach is dependent on the ability of patients to access engaging high-quality information, but significant variability in the quality of web-based information often forces patients to navigate large quantities of inaccurate, incomplete, irrelevant, or inaccessible content. Personalization positions the patient at the center of health care models by considering their needs, preferences, goals, and values. However, the traditional methods used thus far in health care to determine the factors of high-quality content for a particular user are insufficient. Machine learning (ML) uses algorithms to process and uncover patterns within large volumes of data to develop predictive models that automatically improve over time. The health care sector has lagged behind other industries in implementing ML to analyze user and content features, which can automate personalized content recommendations on a mass scale. With the advent of big data in health care, which builds comprehensive patient profiles drawn from several disparate sources, ML can be used to integrate structured and unstructured data from users and content to deliver content that is predicted to be effective and engaging for patients. This enables patients to engage in their health and support education, self-management, and positive behavior change as well as to enhance clinical outcomes.

You May Also Like... Privacy: Recommendation Systems Meet PIR

AbstractWe describe the design, analysis, implementation, and evaluation of Pirsona, a digital content delivery system that realizes collaborative-filtering recommendations atop private information retrieval (PIR). This combination of seemingly antithetical primitives makes possible—for the first time—the construction of practically efficient e-commerce and digital media delivery systems that can provide personalized content recommendations based on their users’ historical consumption patterns while simultaneously keeping said consumption patterns private. In designing Pirsona, we have opted for the most performant primitives available (at the expense of rather strong non-collusion assumptions); namely, we use the recent computationally 1-private PIR protocol of Hafiz and Henry (PETS 2019.4) together with a carefully optimized 4PC Boolean matrix factorization.

Personalized content recommendations on smart TV: Challenges, opportunities, and future research directions

Web recommender systems play a significant role in different domains, such as movies, books, music, etc., and contributes to not only user satisfaction but also to e-business and e-commerce. It utilizes the user’s profile information, preferences, and activities for recommendations of different objects. However, the distinct nature of watching smart TV greatly affects the accuracy and efficiency of recommender systems due to the reasons that it is a lean-back and multi-user device. Hence, the predictions and calculations of a user’s profile, preferences, and activities cannot be accurately utilized by the recommender systems for recommendations to the exact viewer(s) watching smart TV. This paper presents a critical review of existing recommender systems in the perspectives of smart TV watching scenarios. It highlights the issues and challenges and presents some research opportunities to deal with it. It further presents a subjective study for validating the highlighted factors that affect the recommendation results specifically on a smart TV. Results show that watching activities on a smart TV is significantly different from other devices, such as smartphones and computers. It further shows that smart TV is a non-personalized device and normally enjoyed in groups. Hence, personalized recommendations on smart TV need further investigation. The paper concludes that the existing recommender systems need further improvement to cope with issues of recommendations on a non-personalized device i.e., smart TV. Improving the recommender system for smart TVs may contribute not only to the viewer’s satisfaction but also to the conversion rate.

Social explorative attention based recommendation for content distribution platforms

In modern social media platforms, an effective content recommendation should benefit both creators to bring genuine benefits to them and consumers to help them get really interesting content. To address the limitations of existing methods for social recommendation, we propose Social Explorative Attention Network (SEAN), a social recommendation framework that uses a personalized content recommendation model to encourage personal interests driven recommendation. SEAN has two versions: (1) SEAN-END2END allows user's attention vector to attend their personalized interested points in the documents. (2) SEAN-KEYWORD extracts keywords from users' historical readings to capture their long-term interests. It is much faster than the first version, more suitable for practical usage, while SEAN-END2END is more effective. Both versions allow the personalization factors to attend to users' higher-order friends on the social network to improve the accuracy and diversity of recommendation results. Constructing two datasets in two languages, English and Spanish, from a popular decentralized content distribution platform, Steemit, we compare SEAN models with state-of-the-art collaborative filtering (CF) and content based recommendation approaches. Experimental results demonstrate the effectiveness of SEAN in terms of both Gini coefficients for recommendation equality and F1 scores for recommendation accuracy.

Multimedia Data Privacy Against Machines

With the explosion of multimedia data, machine learning applications are proliferating. Privacy concerns have recently resurfaced, due to a few prominent incidents of user data leakage, which divulged sensitive information to other people. Multimedia data platform providers––social networks, their affiliates or governments with access to users’ content––use algorithms to profile users by extracting or inferring demographic information, personality traits, relationships, opinions, and beliefs. These results, in turn, feed algorithms for targeted advertising but also for personalized content recommendation to maximize user engagement. While this is ostensibly for users’ benefit, they have serious privacy implications. We highlight this new problem of privacy protection against machines in contrast to the traditional problem of privacy protection against humans. We briefly touch upon our initial solution, a human-sensitivity-aware image perturbation model, which is able to modify the computational classification results of sensitive attributes while preserving the remaining attributes. We then point to many exciting open problems in this new area.

Mining Web Analytics Data for Information Wikis to Evaluate Informal Learning

Information wikis and especially Wikipedia have become one of the most attractive environments for informal learning. The nature of wikis enables learners to freely navigate the learning environment and independently con-struct knowledge without being required to follow a predefined learning path in line with the constructivist learning theory. Link-based navigation and keyword-based search methods used on Wikipedia and similar information wikis suffer from many limitations. In our paper, we present an effective recommendation system that provides easier and faster access to relevant content on Wikipedia to support informal learning. In addition, we evaluate the impact of personalized content recommendations on informal learning from Wikipedia and show how web analytics data can be used to get an in-sight on informal learning in similar environments.