Problem Of Information Overload Research Articles

Movie Recommendation System using RNN Approach

The problem of information overload has been made worse by the quick advancement of information technology and the quick expansion of the Internet. In response to this issue, recommender systems have developed, assisting users in finding relevant material. The study of personalized recommendation services has seen a change in focus as a result of the complexity of the social setting. We present a novel recommendation approach based on social network recurrent neural network in order to address the sparsity problem of recommender systems while enhancing their accuracy and diversity in complicated scenarios. Using this approach, we group users and take a number of intricate criteria into account.

Context-Aware Music Recommendation Algorithm Combining Classification and Collaborative Filtering

As an effective solution to the problem of information overload, personalized recommendations have received widespread attention in the music field. A context-aware music recommendation algorithm combining classification and collaborative filtering is proposed based on user context information. Firstly, the similarity analysis of the user situation is carried out. A preliminary list of recommended songs is obtained by collaborative filtering. The machine learning method is used to classify music in different scenes to get the preferences of music types in different situations. Finally, the recommendation list obtained by collaborative filtering is combined with the music type preference obtained by the classification model and personalized music recommendations for users in different situations. This algorithm not only effectively reduces the complexity of the recommendation process. Experiments show that the proposed algorithm can effectively improve the accuracy of users' music recommendations.

A Sequential Recommendation Model for Balancing Long- and Short-Term Benefits

Typically, user behaviour occurs continuously, and considering this dynamic sequence correlation can lead to more accurate recommendations. Sequential recommendation systems have, therefore, become an important means of solving the problem of network information overload. However, existing attention mechanisms are still insufficient for modelling users’ dynamic and diverse preferences. This paper presents a recommendation model based on a multiheaded self-attention mechanism and multitemporal embeddings of long- and short-term interests (MSMT-LSI). MSMT-LSI balances users’ long- and short-term benefits through two multihead self-attention networks and finally forms a hybrid representation for recommendation. After finding the most suitable parameter combinations for the MSMT-LSI model through parameter sensitivity analysis and verifying the advantages of the long- and short-term fusion strategy, related experiments on five well-known datasets and their analysis shows that the performance of MSMT-LSI is better than that of the classical model on the same benchmark dataset.

Intelligent recommendation system for College English courses based on graph convolutional networks

With the rapid development of international communication, the number of English courses has shown an explosive growth trend, which has caused a serious problem of information overload, resulting in poor teaching performance of recommended English courses. To solve this problem, this paper proposes a graph convolutional neural network model based on College English course texts, students’ major, English foundation and network structure characteristics. First, by analyzing the relevant data of College English courses and combining with graph neural network, an English course recommendation algorithm model based on the College English learning strategy of proximity comparison is proposed. Then, the College English texts are taken as feature input, and multi-layer graph convolutional neural network is used to process the above graph neural network structure. Attention mechanism is introduced to enhance the representation of graph features in College English skills. Finally, multi-layer attention model is used to process the courses that users have learned, and intelligent course recommendation is made by combining the multi-layer attention modeling of College English skills. The experimental data show that the proposed method achieves the best performance compared with the commonly used College English course recommendation method.

An Anomaly Detection Approach Based on Bidirectional Temporal Convolutional Network and Multi-Head Attention Mechanism

Anomaly detection aims at detecting the data instances that deviate from the majority of data, and it is widely used in various fields for its ability to ensure the quality of the overall data. However, traditional anomaly detection methods face the problems such as low efficiency due to high data complexity and lack of data labels. At the same time, most methods only learn the forward features of time-series data, while lacking attention to the reverse features. For these disadvantages, this paper designs an anomaly detection approach called BiTCN-MHA based on the bidirectional temporal convolutional network (BiTCN) and multi-head attention (MHA) mechanism, which learns the features of anomalous data by capturing the forward and reverse temporal features in the time-series data, as well as solves the problems of feature information overload and neuron “death” by using MHA mechanism and ELU activation function, respectively, thereby quickly and accurately detecting anomalous data. Extensive experiments on six public datasets show that compared with eight state-of-the-arts, the proposed BiTCN-MHA method can improve the precision, recall, AUC and F1-Score by about 6.10%, 10.16%, 4.06% and 8.50%, respectively, especially having better detection performance on small time-series data.

Heterogeneous Graph Neural Network with Personalized and Adaptive Diversity for News Recommendation

The emergence of online media has facilitated the dissemination of news, but has also introduced the problem of information overload. To address this issue, providing users with accurate and diverse news recommendations has become increasingly important. News possesses rich and heterogeneous content, and the factors that attract users to news reading are varied. Consequently, accurate news recommendation requires modeling of both the heterogeneous content of news and the heterogeneous user-news relationships. Furthermore, users’ news consumption is highly dynamic, which is reflected in the differences in topic concentration among different users and in the real-time changes in user interests. To this end, we propose a Heterogeneous Graph Neural Network with Personalized and Adaptive Diversity for News Recommendation (DivHGNN). DivHGNN first represents the heterogeneous content of news and the heterogeneous user-news relationships as an attributed heterogeneous graph. Then, through a heterogeneous node content adapter, it models the heterogeneous node attributes into aligned and fused node representations. With the proposed attributed heterogeneous graph neural network, DivHGNN integrates the heterogeneous relationships to enhance node representation for accurate news recommendations. We also discuss relation pruning, model deployment, and cold-start issues to further improve model efficiency. In terms of diversity, DivHGNN simultaneously models the variance of nodes through variational representation learning for providing personalized diversity. Additionally, a time-continuous exponentially decaying distribution cache is proposed to model the temporal dynamics of user real-time interests for providing adaptive diversity. Extensive experiments on real-world news datasets demonstrate the effectiveness of the proposed method.

The Correlation Between Social App Use and Individual Information Anxiety in the Information Age and the Solutions

Social applications are widely used in the information age. However, the frequent use of social media has brought about the problem of information overload. As a result, individual information anxiety has gradually become a focus of attention. This information overload can hurt individual mental health. The relationship between it and individual information anxiety needs to be further explored. This paper aims to explore the relationship between information overload and individual information anxiety in social applications and propose solutions. The study found the negative impact of information overload on individual mental health in social applications. The methods of alleviating individual information anxiety are put forward respectively from two aspects: individual and media. On the one hand, individuals can deal with the anxiety brought by social media by controlling the time they use social media, choosing reliable information sources and improving information literacy. On the other hand, social media should be censored, provide meaningful interaction, and meet user needs to jointly address the impact of social apps.

A Comprehensive Survey on Recommender Systems Techniques and Challenges in Big Data Analytics with IOT Applications

Purpose: Purpose of this research is to carry out survey on Recommendation systems techniques in Big Data Analytics. This article presents designing of recommender systems and evaluates it with help of various performance metrics in IoT applications.
 
 Theoretical framework: With fast development and applications of Internet of Things, large amount of user data is generated and accumulated every day. Growth of media consumption in online social networks is exponential which requires an efficient and effective recommendation system to enhance excellence in experience for users. Recommender systems help users to overcome Information Overload problem by providing them relevant contents. 
 
 Method/design/approach: The main aspect of recommender system is how to take complete advantage of this ubiquitous data. Recommender system is mainly used to guess or predict users’ interests and make relevant recommendations. Collaborative filtering is the technique that uses the relationships between users and between items in order to build a prediction. Collaborative filtering algorithms are mainly categorized as model-based methods and memory-based methods. In this article, various methods to build recommender system are described. Similarly, Collaborative filtering uses Pearson cosine, cosine vector, Jaccard similarity to identify same users or items. Recommender system has various applications in domain such as healthcare, transportation, agriculture, e-media etc. 
 
 Findings: Evaluation of recommender system with help of metrics such as Precision and Recall is presented. Comparison of experimental results is presented with help of MAE and RMSE. Recommendation system helps to discover relevant insights and can be one of the vital technologies in future IoT solutions.
 
 Research, Practical & social implications: The research makes significant contribution by providing survey of existing recommender systems along with challenges faced while designing effective and accurate recommender. Various similarity measures to find similar users or items are investigated with future pointer direction. Recommender system help in decision making process.
 
 Originality/value: The results and conclusion obtained in this research are helpful in development of novel Recommender systems which definitely assist users to overcome Information Overload issue. It helps user to save network load as well.

BIKAGCN: Knowledge-Aware Recommendations Under Bi-layer Graph Convolutional Networks

Recommender systems are a popular solution for the problem of information overload, offering personalized recommendations to users. Recent years, research has aimed to enhance recommender systems by employing knowledge graphs in conjunction with Graph convolutional network (GCN) to extract user and item features. Although GCN possess a great potential, they are still far from reaching their full capability in recommender systems. This paper introduces a novel approach—knowledge-aware recommendations under bi-layer graph convolutional networks (BIKAGCN) that combines attention and bi-layer GCNs to improve performance. The first layer of the BIKAGCN model trains embedding representations of users and items based on user-item interaction graphs. The second layer introduces a novel knowledge-aware layer of attention and graph convolutional network (KAGCN) layer that leverages both the first layer’s user-item embeddings and item knowledge graph embeddings. Experimental results on three publicly available datasets (MovieLens-20M, Last-FM, and Book-Crossing) demonstrate that BIKAGCN leads to significant performance improvements in recall@20 metric (14.41%, 8.86%, and 20.90%, respectively) compared to currently available state-of-the-art approaches. Moreover, the model maintains satisfactory performance in cold-start cases.The research provides some guidance for the direction of subsequent research on recommender systems.

DSMN: An Improved Recommendation Model for Capturing the Multiplicity and Dynamics of Consumer Interests

Electronic shopping has become an important way of shopping in our daily life. A good recommendation system can greatly improve the consumer shopping experience through alleviating consumer and product information overload problem. In the real scenario, each consumer has a wide variety of interests, and consumers interests are constantly changing. Many recent models usually give an overall embedding for consumer behavior sequence or only capture features of customers and characteristics of products from single perspective. However, these methods can hardly model consumers multiple interests and evolving of interests completely. Thus, in this study, we consider both the diversity and dynamics of consumer interests, and model them in a unified model. In light of this, we propose a novel model named Deep Self-Attention Mulit-Interest Networks (DSMN), which captures latent multiple interests by using capsule networks. It can perceive evolving of interests from consumer behavior sequences by using attention mechanism. We evaluate our model on three real-world datasets. In the experiments, our DSMN is 4.934% higher than the average of competitors in terms of AUC. Compared to our original silver medal method on the Kaggle competition, our DSMN is 18.978% higher in terms of MAP@12.

An extended study of a cloud-based recommender system for competitive programming platforms with machine and deep learning

Nowadays, online judges are very important to improve programming skills for education and technology companies. For this reason, there are many online judges that include large sets of programming challenges. This creates an information overload problem that affects students due to their lack of expertise in choosing the correct challenge to solve, resulting in frustration and a loss of interest in this topic. To solve this scenario, recommender systems appear, but programming judges have not delved much into it. Consequently, this research aims to evaluate the performance of six selected collaborative filtering techniques via a cloud-based software architecture. To validate our experiments we used real online programming judges like CodeChef and NinjaCoding using cloud based architecture with Amazon Web Services, evaluated through Friedman and Wilcoxon statistical tests. The results indicated that Singular Value Decomposition is the best model evaluated with RMSE metric and the fastest in execution time with big datasets.

The Impact of Social Media on Consumer Sentiment

With the wide use of network media, consumers’ information acquisition and communication methods have undergone significant changes. Network media not only provides them with a fast and efficient way to obtain information, but also provides them with a broader social space and communication platform. Therefore, the popularity of social media has brought some negative effects, such as low mood and depression among consumers. This paper will first study the impact of social media operation strategies on consumer sentiment. Secondly, the author will examine how social media self-control affects mood, which includes how users control their emotions and behaviors on social media, and the impact of this self-control on consumer mood. This paper will discuss and study through the research methods of psychology and sociology. Finally, this study aims to analyze the problem of information overload between social media and consumer sentiment by focusing on the comments made by consumers on social media. At the end of the study, the research results need to be summarized and analyzed in order to provide useful references for social media platforms to manage consumer emotions, so as to provide useful references for lawmakers, academics and practitioners.

A bitwise approach on influence overload problem

Increasingly developing online social networks has enabled users to send or receive information very fast. However, due to the availability of an excessive amount of data in today’s society, managing the information has become very cumbersome, which may lead to the problem of information overload. This highly eminent problem, where the existence of too much relevant information available becomes a hindrance rather than a help, may cause losses, delays, and hardships in making decisions. Thus, in this paper, by defining information overload from a different aspect, we aim to maximize the information propagation while minimizing the information overload (duplication). To do so, we theoretically present the lower and upper bounds for the information overload using a bitwise-based approach as the leverage to mitigate the computation complexities and obtain an approximation ratio of 1−1e. We propose two main algorithms, B-square and C-square, and compare them with the existing algorithms. Experiments on two types of datasets, synthetic and real-world networks, verify the effectiveness and efficiency of the proposed approach in addressing the problem.

Efficient Course Recommendation using Deep Transformer based Ensembled Attention Model

The exponential development of online learning resources has led to an information overload problem. Therefore, recommender systems play a crucial role in E-learning to provide learners with personalised course recommendations by automatically identifying their preferences. In addition, e-Learning platforms such as MOOCs and LMS have been criticised for their low course completion rates, and one of the primary reasons is that they do not provide personalised course recommendations for users with varying interests. Rapidly locating the courses that users are interested in on enormous e-Learning platforms can have a significant impact on the quality of learning and the dissemination of knowledge to the learner. This paper examines the most prevalent recommendation techniques utilised in E-learning. We examined how to apply Deep Transformer based Ensembled Attention Model (DTEAM) on e-Learning system in order to achieve personalized course recommendations. The proposed recommendation model uses BERT as its foundation integrated MLM and Transformers. Predicted course recommendations are more aligned with the interests of users. Our experimental results proved that traditional recommendation algorithms, such as collaborative filtering and item-based filtering are incapable of producing superior results. The consequence of the research can assist students in selecting courses according to their preferences and improve their learning caliber

PERCEPTIONS AND EXPERIENCES OF M-HEALTH TECHNOLOGIES AND SOCIAL MEDIA: A QUALITATIVE STUDY IN PATIENTS WITH SCHIZOPHRENIA

The growing influence of technology in the realm of mental health presents promising prospects for patients with psychiatric disorders like schizophrenia. The objective of this study is to investigate the perceptions of individuals with schizophrenia spectrum disorder regarding the utilization of technology and social media. The qualitative methodology used consists of an individual structured interview and the data were subjected to thematic analysis. The results show that the participants use digital tools for various activities such as work, searching for information, entertainment, and socialising. Their perceptions confirm the usefulness and easy accessibility of these tools, which enable positive changes in the organisation of daily life and social relationships. In general, it is possible to identify both positive and negative aspects of technology and social media, such as abuse and addictive behaviour, network and information overload problems, and the risk that privacy may not be guaranteed; among the positive ones, the promotion of remote sociability, speed in carrying out tasks and acquiring useful information emerge. Most of the participants express a positive perception of the possibility that digital tools can help in the field of mental health.

A Comprehensive Survey on Recommender Systems Techniques and Challenges in Big Data Analytics with IoT Applications

Purpose: Purpose of this research is to carry out survey on Recommendation systems techniques in Big Data Analytics. This article presents designing of recommender systems and evaluates it with help of various performance metrics in IoT applications.
 
 Theoretical framework: With fast development and applications of Internet of Things, large amount of user data is generated and accumulated every day. Growth of media consumption in online social networks is exponential which requires an efficient and effective recommendation system to enhance excellence in experience for users. Recommender systems help users to overcome Information Overload problem by providing them relevant contents. 
 
 Method/design/approach: The main aspect of recommender system is how to take complete advantage of this ubiquitous data. Recommender system is mainly used to guess or predict users’ interests and make relevant recommendations. Collaborative filtering is the technique that uses the relationships between users and between items in order to build a prediction. Collaborative filtering algorithms are mainly categorized as model-based methods and memory-based methods. In this article, various methods to build recommender system are described. Similarly, Collaborative filtering uses Pearson cosine, cosine vector, Jaccard similarity to identify same users or items. Recommender system has various applications in domain such as healthcare, transportation, agriculture, e-media etc. 
 
 Findings: Evaluation of recommender system with help of metrics such as Precision and Recall is presented. Comparison of experimental results is presented with help of MAE and RMSE. Recommendation system helps to discover relevant insights and can be one of the vital technologies in future IoT solutions.
 
 Research, Practical & social implications: The research makes significant contribution by providing survey of existing recommender systems along with challenges faced while designing effective and accurate recommender. Various similarity measures to find similar users or items are investigated with future pointer direction. Recommender system help in decision making process.
 
 Originality/value: The results and conclusion obtained in this research are helpful in development of novel Recommender systems which definitely assist users to overcome Information Overload issue. It helps user to save network load as well.

A Study of Digital Museum Collection Recommendation Algorithm Based on Improved Fuzzy Clustering Algorithm

With the rapid advancement of internet technology, various industries have accumulated vast amounts of data, including on user behavior and personal preferences. Traditional museums can leverage this user data to uncover individual preferences and offer personalized services to their visitors. However, the exponential growth of information has also led to the problem of information overload, making it challenging for users to find relevant information within the vast data landscape. Consequently, the utilization rate of available information decreases. By harnessing the power of cloud computing, big data analytics, and recommendation systems, museums can enhance visitors’ touring experiences by helping them discover collections aligned with their interests and connecting with like-minded individuals. To address this objective, the research focuses on optimizing the initial clustering centers of the fuzzy clustering algorithm and parallelizing the optimized algorithm using MapReduce, resulting in the development of a novel MapReduce-based k-prototype fuzzy c-means (MRKPFCM) algorithm. Subsequently, the MRKPFCM algorithm is combined with the classical collaborative filtering algorithm to create a hybrid and parallelized collaborative filtering recommendation algorithm, incorporating elements such as MRKPFCM, audience, and collection. This hybrid algorithm is further supplemented by a content-based recommendation approach to generate comprehensive and refined recommendation results. Experimental findings demonstrate that the predictive scoring errors, as measured by RMSE and MAE, exhibited a downward trend when the number of nearest neighbors for target users fell within the range of 10–20. For instance, the studied algorithm’s MAE value decreased from 0.7512 to 0.7179, surpassing the corresponding figures for the two comparison algorithms. Moreover, with an increase in the number of nearest neighbors within the same range, all three algorithms experienced improved accuracy in prediction results. In particular, the accuracy rate rose from 17.84% to 18.82%, outperforming the two comparison algorithms. In summary, the enhanced hybrid recommendation algorithm achieved through this study displays superior recommendation accuracy and holds significant practical value.

Critical zone science in the Western US—Too much information?

Exponentially growing publication rates are increasingly problematic for interdisciplinary fields like Critical Zone (CZ) science. How does one “keep up” across different, but related fields with unique hypotheses, field techniques, and models? By surveying CZ academics in the Western US, a region with substantial CZ research, we document the challenge. While conventional knowledge synthesis products-particularly review papers clearly support knowledge transfer, they are static and limited in scope. More informal paths for knowledge transfer, including social networking at conferences and academic mentorship, are useful but are unstructured and problematic for young scientists or others who may not have access to these resources. While new machine-learning tools, including ChatGPT, offer new ways forward for knowledge synthesis, we argue that they do not necessarily solve the problem of information overload in CZ Science. Instead, we argue that what we need is a community driven, machine aided knowledge tool that evolves and connects, but preserves the richness of detail found in peer-reviewed papers. The platform would be designed by CZ scientists, machine-aided and built on the strengths of people-driven synthesis. By involving the scientist in the design of this tool, it will better reflect the practice of CZ science-including hypothesis generation, testing across different time and space scales and in different time periods and locations, and, importantly, the use and evaluation of multiple, often sophisticated methods including fieldwork, remote sensing, and modeling. We seek a platform design that increases the findability and accessibility of current working knowledge while communicating the CZ science practice.

Entity Summarization via Exploiting Description Complementarity and Salience.

Entity summarization is a novel and efficient way to understand real-world facts and solve the increasing information overload problem in large-scale knowledge graphs (KG). Existing studies mainly rely on ranking independent entity descriptions as a list under a certain scoring standard such as importance. However, they often ignore the relatedness and even semantic overlap between individual descriptions. This may seriously interfere with the contribution judgment of descriptions for entity summarization. Actually, the entity summary is a whole to comprehensively integrate the main aspects of entity descriptions, which could be naturally treated as a set. Unfortunately, the exploration of these set characteristics for entity summarization is still an open issue with great challenges. To that end, we draw inspiration from a set completion perspective and propose an entity summarization method with complementarity and salience (ESCS) to deeply exploit description complementarity and salience in order to form a summary set for the target entity. Specifically, we first generate entity description representations with textual features in the description embedding module. For the purpose of learning complementary relationships within the entire summary set, we devise a bi-directional long short-term memory structure to capture global complementarity for each summary in the summary complementarity learning module. Meanwhile, in order to estimate the salience of individual descriptions, we calculate similarities between semantic embeddings of the target entity and its property-value pairs in the description salience learning module. Next, with a joint learning stage, we can optimize ESCS from a set completion perspective. Finally, a summary generation strategy is designed to infer the entire summary set step-by-step for the target entity. Extensive experiments on a public benchmark have clearly demonstrated the effectiveness of ESCS and revealed the potential of set completion in entity summarization task.

A Study of the Impact of Information Overload in Social Media in the Simple Medium Network - The Case of the University Students Majoring in Communication Studies

Social media has grown rapidly in recent years, and in the current context of growing social media addiction and information overload, it is urgent and important to study the impact of information overload on young people. Social media is closely linked to the daily lives of young people, and the problem of information overload can induce various conflicts and problems. It directly affects the development of individuals. While previous research has highlighted the negative effects of information overload on peoples mental health, this paper focused on the subjectivity of social media users. The study used in-depth interviews with a sample of 10 current university students majoring in media. An understanding of their perceived information overload and attitudes was obtained. The findings suggest that while information overload creates an information wall and offers more possibilities to see the world, social media subjectivity can be very powerful. However, the subjectivity of social media users can be a good way to deal with the effects of information overload and make good use of it. This study extends the research on this issue and highlights the subjective nature of social media users.