Problem Of Information Overload Research Articles

Graph and Sequential Neural Networks in Session-based Recommendation: A Survey

Recent years have witnessed the remarkable success of recommendation systems (RSs) in alleviating the information overload problem. As a new paradigm of RSs, session-based recommendation (SR) specializes in users’ short-term preferences and aims at providing a more dynamic and timely recommendation based on ongoing interactions. This survey presents a comprehensive overview of the recent works on SR. First, we clarify the key definitions within SR and compare the characteristics of SR against other recommendation tasks. Then, we summarize the existing methods in two categories: sequential neural network based methods and graph neural network (GNN) based methods. The relevant frameworks and technical details are further introduced. Finally, we discuss the challenges of SR and new research directions in this area.

Independence and synergy of spatial attention in the two visual systems of jumping spiders.

By selectively focusing on a specific portion of the environment, animals can solve the problem of information overload, toning down irrelevant inputs and concentrating only on the relevant ones. This may be of particular relevance for animals such as the jumping spider, which possess a wide visual field of almost 360deg and thus could benefit from a low-cost system for sharpening attention. Jumping spiders have a modular visual system composed of four pairs of eyes, of which only the two frontal eyes (the anteromedial eyes, AMEs) are motile, whereas the other secondary pairs remain immobile. We hypothesised that jumping spiders can exploit both principal and secondary eyes for stimulus detection and attentional shift, with the two systems working synergistically. In experiment 1, we investigated the attentional responses of AMEs following a spatial cue presented to the secondary eyes. In experiment 2, we tested for enhanced attention in the secondary eyes' visual field congruent with the direction of the AMEs' focus. In both experiments, we observed that animals were faster and more accurate in detecting a target when it appeared in a direction opposite to that of the initial cue. In contrast with our initial hypothesis, these results would suggest that attention is segregated across eyes, with each system working on compensating the other by attending to different spatial locations.

Personalized multi-head self-attention network for news recommendation

With the rapid explosion of online news and user population, personalized news recommender systems have proved to be efficient ways of alleviating information overload problems by suggesting information which attracts users in line with their tastes. Exploring relationships among words and news is critical to structurally model users’ latent tastes including interested domains, while selecting informative words and news can directly reflect users’ interests. Most of the current studies do not provide an effective framework that combines distilling users’ interested latent spaces and explicit points systematically. Moreover, introducing more advanced techniques to merely chase accuracy has become a universal phenomenon. In this study, we design a Personalized Multi-Head Self-Attention Network (PMSN) for news recommendation, which combines multi-head self-attention network with personalized attention mechanism from both word and news levels. Multi-head self-attention mechanism is used to model interactions among words and news, exploring latent interests. Personalized attention mechanism is applied by embedding users’ IDs to highlight informative words and news, which can enhance the interpretability of personalization. Comprehensive experiments conducted using two real-world datasets demonstrate that PMSN efficiently outperforms state-of-the-art methods in terms of recommendation accuracy, without complicated structure design and exhausted even external resources consumption. Furthermore, visualized case study validates that attention mechanism indeed increases the interpretability.

What motivates knowledge sharing? Evaluating the quality of answer contribution in online Q&A communities

The Online Q&A community provides platforms for Internet users to exchange and share knowledge. Its rapid development has caused the problem of information overload, which promotes users' demand for precise and personalized information. In light of this, we innovatively analyze the correlation between knowledge quality and user behaviors. First, we propose a novel approach method to evaluate the answer quality through a machine learning approach, which applies the information adoption theory to the design of a text classification model. Hereafter, with applications of motivation crowding theory, this paper introduces the classification results of answer quality by machine learning algorithms into the empirical research model to explore the factors that motivate users in both participation and high-quality user-generated content creation. Results show that both extrinsic and intrinsic factors determine the quality of knowledge contributors' answers. Further, certain external interventions (monetary rewards) can crowd out the effects of knowledge contributors’ intrinsic motivations (knowledge self-efficacy). It enriches the research on user knowledge contribution behavior in online Q&A communities and also provides theoretical guidance and suggestions for community operation.

Algorithmic Recommender Systems

Abstract Despite their ethical challenges, recommender systems (RS) are widely endorsed as a necessary solution to the problem of information overload. After clarifying how the harmful effects of information overload can be characterized in distinct ways, I explore the often overlooked potential benefits of abundant online spaces. I argue that these spaces afford valuable opportunities to experience spontaneous freedom. I then put forth a more comprehensive evaluation of the role RS should assume in algorithmically structuring the online space. This evaluation aims to reconcile the dual objectives of mitigating the harms of information overload and preserving opportunities to experience spontaneous freedom.

The Research on Intelligent News Advertisement Recommendation Algorithm Based on Prompt Learning in End-to-End Large Language Model Architecture

With the explosive growth of information on the internet, users are increasingly facing the problem of information overload, making precise news and ad recommendations an important area of research. While traditional recommendation algorithms can meet user needs to some extent, they still have limitations in dealing with complex and changing user behaviors and dynamic content environments. This paper addresses the shortcomings of existing news and ad recommendation systems by proposing an intelligent recommendation algorithm based on an end-to-end large language model architecture. Firstly, we utilize the BERT model as the foundation, leveraging its powerful text representation capabilities to achieve deep semantic understanding of news and ad content, thereby capturing more detailed content features. Secondly, we apply prompt learning to fine-tune the BERT model, designing specific prompts for the model to better understand the implicit needs and preferences of users. Finally, we integrate these steps into an end-to-end architecture, enabling the model to achieve automated learning and optimization throughout the entire process from input to output, thus improving the precision and efficiency of recommendations. Experimental results demonstrate that the proposed method significantly outperforms traditional methods in the task of news and ad recommendation, not only enhancing the accuracy and relevance of recommendations but also effectively improving the model's interpretability and flexibility. This research explores new possibilities for the application of large language models in recommendation systems.

Distribution-aware hybrid noise augmentation in graph contrastive learning for recommendation

The recommender systems are one of the most effective big data tools for solving the information overload problem, but data sparsity greatly affects its performance. However, most of the existing graph-based contrastive learning methods perturb the original graph structure for data augmentation, which may lead to semantic loss and subsequently degrade the recommendation performance. Meanwhile, studies have shown that improving the uniformity of data distribution is more effective than data augmentation using graph perturbation. In this paper, to enhance the uniformity of data distribution, we propose Distribution-Aware Hybrid Noise augmentation in graph contrastive learning for Recommendation (DAHNRec). Specifically, we design distribution-aware hybrid noise augmentation (DAHN) method to generate noise suitable for the users and items, instead of simply utilizing random noise following uniform distributions. By applying DAHN, the model can learn the appropriate noise distribution during the training process to enhance the uniformity of the embedding space. Additionally, we propose Balanced Bayesian Personalized Ranking (BBPR) as the loss function for recommendation tasks to improve difference characterizing between positive and negative samples. This improves the ranking tasks’ performance. Furthermore, we conduct extensive experiments based on public benchmark datasets. The results indicate that DAHNRec outperforms several existing methods. Furthermore, DAHNRec is a general recommendation model based on user–item interactions, which can be used to improve the recommendation performance in various real-world scenarios, such as e-commerce platforms and social media.

MOOCs video recommendation using low-rank and sparse matrix factorization with inter-entity relations and intra-entity affinity information

PurposeThe serious information overload problem of MOOCs videos decreases the learning efficiency of the students and the utilization rate of the videos. There are two problems worthy of attention for the matrix factorization (MF)-based video learning resource recommender systems. Those methods suffer from the sparsity problem of the user-item rating matrix, while side information about user and item is seldom used to guide the learning procedure of the MF. MethodTo address those two problems, we proposed a new MOOCs video resource recommender LSMFERLI based on Low-rank and Sparse Matrix Factorization (LSMF) with the guidance of the inter-Entity Relations and intra-entity Latent Information of the students and videos. Firstly, we construct the inter-entity relation matrices and intra-entity latent preference matrix for the students. Secondly, we construct the inter-entity relation matrices and intra-entity affinity matrix for the videos. Lastly, with the guidance of the inter-entity relation and intra-entity affinity matrices of the students and videos, the student-video rating matrix is factorized into a low-rank matrix and a sparse matrix by the alternative iteration optimization scheme. ConclusionsExperimental results on dataset MOOCcube indicate that LSMFERLI outperforms 7 state-of-the-art methods in terms of the HR@K and NDCG@K(K = 5,10,15) indicators increased by an average of 20.6 % and 21.0 %, respectively.

A Hybrid News Recommendation Approach Based on Title–Content Matching

Personalized news recommendation can alleviate the information overload problem, and accurate modeling of user interests is the core of personalized news recommendation. Existing news recommendation methods integrate the titles and contents of news articles that users have historically browsed to construct user interest models. However, this method ignores the phenomenon of “title–content mismatching” in news articles, which leads to the lack of precision in user interest modeling. Therefore, a hybrid news recommendation method based on title–content matching is proposed in this paper: (1) An interactive attention network is employed to model the correlation between title and content contexts, thereby enhancing the feature representation of both; (2) The degree of title–content matching is computed using a Siamese neural network, constructing a user interest model based on title–content matching; and (3) neural collaborative filtering (NCF) based on factorization machines (FM) is integrated, taking into account the perspective of the potential relationships between users for recommendation, leveraging the insensitivity of neural collaboration to news content to alleviate the impact of title–content mismatching on user feature modeling. The proposed model was evaluated on a real-world dataset, achieving an nDCG of 83.03%, MRR of 81.88%, AUC of 85.22%, and F1 Score of 35.10%. Compared to state-of-the-art news recommendation methods, our model demonstrated an average improvement of 0.65% in nDCG and 3% in MRR. These experimental results indicate that our approach effectively enhances the performance of news recommendation systems.

Recommender Systems for Teachers: A Systematic Literature Review of Recent (2011–2023) Research

Recommender Systems (RSs) have recently emerged as a practical solution to the information overload problem users face when searching for digital content. In general, RSs provide their respective users with specialized advice and guidance in order to make informed decisions on the selection of suitable digital content. This paper is a systematic literature review of recent (2011–2023) publications on RSs designed and developed in the context of education to support teachers in particular—one of the target groups least frequently addressed by existing RSs. A body of 61 journal papers is selected and analyzed to answer research questions focusing on experimental studies that include RS evaluation and report evaluation results. This review is expected to help teachers in better exploiting RS technology as well as new researchers/developers in this field in better designing and developing RSs for the benefit of teachers. An interesting result obtained through this study is that the recent employment of machine learning algorithms for the generation of recommendations has brought about significant RS quality and performance improvements in terms of recommendation accuracy, personalization and timeliness.

Guest Editorial for the Special Issue “New Trends in Algorithms for Intelligent Recommendation Systems”

Currently, the problem of information overload, a term popularized by Alvin Toffler in his book Future Shock [...]

CTITF: A tensor factorization model with constrained bidirectional user trust and implicit feedback for context-aware recommender systems

Recommender systems offer an efficient solution to the problem of information overload, which is exacerbated by the rapid expansion of data. Context-aware recommender systems (CARS) have become prominent, incorporating contextual information to provide more desirable recommendations. While the dimension of contextual information rises, the complexity of algorithms increases as well, highlighting the data sparsity problem. In this paper, we propose CTITF, a novel tensor factorization model that incorporates constrained user bidirectional trust and implicit feedback. First, CTITF distinguishes the roles of trust networks in terms of the directionality of trust relationships: the trustor and the trustee. CTITF then constructs trust influences and biases for both roles to linearly represent user preferences. Furthermore, CTITF improves the rating prediction function and recommendation objective function based on CP factorization. Finally, CTITF filters out trust relationships where users do not have the same history items. It also uses contextual information to limit the amount of implicit feedback, addressing the issue of high time complexity. Experiments on two real-world datasets illustrate that the proposed model outperforms previous TF-based models and social recommender systems in terms of rating prediction accuracy. In addition, the CTITF model reduces the training time by 50.28% compared to the trustTF model, which also uses trust.

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.