Problem Of Information Overload Research Articles

Multimodal trust based recommender system with machine learning approaches for movie recommendation

Recommender system (RS) are a type of suggestion to the information overload problem suffered by user of websites that allow the rating of particular item. The movie RS are one of the most efficient, useful, and widespread applications for individual to watch movie with minimum decision time. Many attempts made by the researchers to solve these issues like watching movie, purchasing book etc., through RS, whereas most of the study fails to address cold start problem, data sparsity and malicious attacks. This study address these problems, we propose trust matrix measure in this paper, which combines user similarity with weighted trust propagation. Non cold user passed through different models with trust filter and a cold user generated an optimal score with their preferences for recommendation. Four different recommendation models such as Backpropagation (BPNN) model, SVD (Singular Value Decomposition) model, DNN (Deep Neural Network model) and DNN with Trust were compared to recommend the suitable movie to the user. Results imply that DNN with trust model proved to be the best model with high accuracy of 83% with 0.74 MSE value and can be used for best movie recommendation.

Design of the Piano Score Recommendation Image Analysis System Based on the Big Data and Convolutional Neural Network.

In the era of big data, the problem of information overload is becoming more and more obvious. A piano music image analysis and recommendation system based on the CNN classifier and user preference is designed by using the convolutional neural network (CNN), which can realize accurate piano music recommendation for users in the big data environment. The piano music recommendation system based on the CNN is mainly composed of user modeling, music feature extraction, recommendation algorithm, and so on. In the recommendation algorithm module, the potential characteristics of music are predicted by the regression model, and the matching degree between users and music is calculated according to user preferences. Then, music that users may be interested in is generated and sorted in order to recommend new piano music to relevant users. The image analysis model contains four “convolution + pooling” layers. The classification accuracy and gradient change law of the CNN under RMSProp and Adam optimal controllers are compared. The image analysis results show that the Adam optimal controller can quickly find the direction, and the gradient decreases greatly. In addition, the accuracy of the recommendation system is 55.84%. Compared with the traditional CNN algorithm, this paper uses the convolutional neural network (CNN) to analyze and recommend piano music images according to users' preferences, which can realize more accurate piano music recommendation for users in the big data environment. Therefore, the piano music recommendation system based on the CNN has strong feature learning ability and good prediction and recommendation ability.

User-based Collaborative Filtering Algorithm Design and Implementation

With the rapid development of social humanities and technology, especially the development of the Internet, the current Internet era, the amount of information is increasing rapidly with explosive growth rate, the information overload problem is particularly obvious, on the accurate acquisition of information from the massive amount of information users want, from which the personalized recommendation technology was born. In order to solve the problem of acquiring the information users want, this paper researches and discusses a kind of personalized recommendation algorithm - a user-based collaborative filtering algorithm, analyzing the user behavior, comparing the advantages and disadvantages of other related algorithms, using the UserCF algorithm, and optimizing the sparse matrix to reduce the time and complexity of the operation. The algorithm is implemented by software to generate recommendation results. The results of the experimental data in this paper show that the algorithm is effective in recommending projects to users.

Collaborative Filtering Algorithm Based on Improved Time Function and User Similarity

As a typical representative of information filtering technology in the era of big data, a recommendation system is an important means to solve the problem of information overload. A collaborative filtering recommendation algorithm is one of the important technologies to realize the recommendation system, but the traditional collaborative filtering algorithm only considers the similarity of ratings between users. As the number of users and the number of items increases, it faces user interest drift and reduced recommendation Precision, and other issues. In this regard, a collaborative filtering algorithm based on improved time function and user similarity is proposed. First, considering that user interests will dynamically change over time, this paper introduces an improved time function in the traditional scoring similarity; secondly, considering the impact of the number of items evaluated by users on the calculation of similarity measurement, this paper takes the Pearson correlation coefficient weighted scoring into account; Finally, the fusion recommendation is based on the improved time function and the weighted Pearson correlation coefficient to improve the Precision of recommendation prediction. This paper conducts simulation experiments on MovieLens 100K and 1M data sets respectively. The results show that, compared with the traditional collaborative filtering recommendation algorithm, combining the improved time function and the weighted Pearson correlation coefficient can effectively improve the recommendation Precision.

Fusion of Internal Similarity to Improve the Accuracy of Recommendation Algorithm

Collaborative filtering algorithms (CF) and mass diffusion (MD) algorithms have been successfully applied to recommender systems for years and can solve the problem of information overload. However, both algorithms suffer from data sparsity, and both tend to recommend popular products, which have poor diversity and are not suitable for real life. In this paper, we propose a user internal similarity-based recommendation algorithm (UISRC). UISRC first calculates the item-item similarity matrix and calculates the average similarity between items purchased by each user as the user’s internal similarity. The internal similarity of users is combined to modify the recommendation score to make score predictions and suggestions. Simulation experiments on RYM and Last.FM datasets, the results show that UISRC can obtain better recommendation accuracy and a variety of recommendations than traditional CF and MD algorithms.

A Thematic Portfolio and Recommended Study on the Usefulness of Online Medical Reviews Based on QCA Methods

The online comment for doctors plays an important role in assisting patients to master the real medical situation and choose medical treatment, which greatly reduces the adverse effects of online medical market information asymmetry. but because ordinary patients lack professional medical knowledge and can not accurately and efficiently write comments that are useful to similar patients, we identify useful topics from online medical reviews and make combination recommendations based on patients with different diseases. guide patients to write comments from the most useful dimensions to alleviate the problem of information overload, thus maximizing the limited human resource utility. This study aimed at online medical platforms such as good doctors and micro-doctors collected online doctor reviews for major diseases and used LDA topic mining and fsQCA fuzzy set qualitative comparative analysis to analyze key topics that affect the usefulness of reviews and optimal topic combinations under different disease types.

A Recommendation Model for College Career Entrepreneurship Projects Based on Deep Learning

The recommendation system is an active, personalized, and real‐time technology platform proposed in the 1990s to solve the problem of information overload. The recommendation system can constantly adjust the recommendation results according to the real‐time behaviors of users. In other words, if the user’s interest changes, the recommendation system can present different information to the user. Therefore, the recommendation system is the best way to solve the problem of information overload in entrepreneurial projects. Based on the ConvMF algorithm, this paper proposes an entrepreneurial project recommendation algorithm based on a deep neural network and matrix decomposition. A deep neural network was established for the extraction of the hidden features of entrepreneurial projects, and a convolution neural network was used to process the text description information of entrepreneurial projects. One‐hot coding was used to process the regional characteristics and financing round characteristics of entrepreneurial projects, and word embedding was used to process the label features of entrepreneurial projects. The implicit features of users are extracted from the user’s rating matrix using matrix decomposition technology. Finally, recommendations are made according to the implicit characteristics of users and the items learned.

Study of online learning resource recommendation based on improved BP neural network

Personalised recommendation has gradually become an effective way to solve the problem of information overload in the era of big data. Therefore, in order to improve the efficiency of online learning, this paper discusses the design of online learning resource recommendation algorithm based on improved BP neural network, and the results show that it has high value for popularisation and application. Based on the transmission network, the improved BP neural network of momentum factor can achieve more efficient data mining. After training learning resources and user data, it can match the real score and the predicted score, so as to ensure the accuracy of personalised recommendation. The main contribution of this paper is to propose a recommendation algorithm to online learning resources through improved BP neural network algorithm, and the feasibility of the algorithm is verified. The research method of this paper provides a reference for the research of personalised recommendation algorithm of online resources.

A Personalized Recommendation for Web API Discovery in Social Web of Things

With the explosive growth of Web of Things (WoT) and social web, it is becoming hard for device owners and users to find suitable web Application Programming Interface (API) that meet their needs among a large amount of web APIs. Social-aware and collaborative filtering-based recommender systems are widely applied to recommend personalized web APIs to users and to face the problem of information overload. However, most of the current solutions suffer from the dilemma of accuracy- diversity where the prediction accuracy gains are typically accompanied by losses in the diversity of the recommended APIs due to the influence of popularity factor on the final score of APIs (e.g., high rated or high-invoked APIs). To address this problem, the purpose of this paper is developing an improved recommendation model called (Personalized Web API Recommendation) PWR, which enables to discover APIs and provide personalized suggestions for users without sacrificing the recommendation accuracy. To validate the performance of our model, seven variant algorithms of different approaches (popularity-based, user-based and item-based) are compared using MovieLens 20M dataset. The experiments show that our model improves the recommendation accuracy by 12% increase with the highest score among compared methods. Additionally it outperforms the compared models in diversity over all lengths of recommendation lists. It is envisaged that the proposed model is useful to accurately recommend personalized web API for users.

GCN-Int: A Click-Through Rate Prediction Model Based on Graph Convolutional Network Interaction

Recommendation system has drawn growing attention in the academia and industry because it can solve the problem of information overload. Among a variety of methods, the click-through rate prediction model plays an important role in predicting user’s attention to a specific item. To predict click-through rate, high-dimensional and sparse features are usually adopted, and the accuracy of the prediction result depends on the combination of high-order features to a great extent. Therefore, many methods have been proposed to find the low-dimensional representation from sparse high-dimensional original features, and the meaningful way of feature combination has also been mined to improve the accuracy of the model. However, the click-through rate prediction models generally have two problems. One is that they can’t extract the feature interaction of non-Euclidean features very well. Another one is that it is hard to explain the inward meaning of feature interaction. In this paper, a GCN-int model based on the interaction of Graph Convolutional Network is proposed to solve the above problems. The proposed model simplifies the complex interaction among multiple features, gets a better representation of the interaction between high-order features, and improves the interpretability of feature interaction. The experimental results on the public movie recommendation dataset and our own IPTV movie recommendation dataset show that the proposed GCN-int model gets higher accuracy and efficiency compared with the state-of-the-art models.

Extracting Keywords from Texts based on Word Frequency and Association Features

With the development of information technology such as mobile Internet and social media applications, network information is growing rapidly and leads to the problem of information overload. Keywords help to filter and find interesting information for users from massive text. Automatic extraction of keywords from text as tags of text help to improve recommendation and keyword-based information retrieval. This paper proposes a novel keyword extraction approach from text that combines features such as word frequency and association. Experiment results show that the precision rate, recall rate and F-measure are all better than those of TextRank and TF-IDF.

Graph-based recommendation by trust

The recommendation system as an effective tool is used to alleviate the information overload problem, and is being applied to personalised services. In recommendation, user's ratings as explicit feedback data can clearly express user's preference, however explicit feedback data has a natural defect that user's interest for an item would varies in context such as emotions, time etc. and the ratings could not reflect the changing. In this paper, a novel graph recommendation algorithm is presented based on user's trust relation that is regarded as implicit feedback data to calculate similarity to enhance the performance for Top-K recommendations. By evaluating the presented algorithm and compared to four competitive algorithms on the four real world datasets, the results show that the presented algorithm performs better than other algorithms in precision, recall and converge.

Modeling Academic Research Collaborator Selection Using an Integrated Model

Expert finding systems try to alleviate the information overload problem and recommend experts who can satisfy users' needs. They support researchers to find research collaborators automatically. The main challenge of current expert finding systems is that they retrieve experts based on the content of their documents but ignore the human interaction perspective. The human interaction perspective comprises the factors that influence collaborator selection decisions in real life. This study aimed to develop a collaborator selection model for expert finding systems in research universities. This model includes human capital, social capital, and cultural capital factors that influence collaborator selection. The researchers integrated the Scientific and Technical Human Capital (STHC) model and Social Capital Theory to determine these factors. The authors conducted a survey comprising 349 researchers from Malaysian research universities to validate the research hypotheses. A partial least squares structural equation model (PLS-SEM) was employed to analyze all the survey data. The empirical results revealed that the significant factors that influence collaborator selection in the research universities context were cognitive accessibility, reliability, relevance, commitment, physical accessibility, cultural experiences, complementary skills, and research experience. Surprisingly, the results revealed that network ties, relational accessibility, and reputation were insignificant factors for collaborator selection. This study proposed a research model for collaborator selection in the research universities context and provided several recommendations for the policymakers and practitioners. The model will help to provide sufficient criteria to select academic research collaborator in universities and can be used by expert finding systems designers, researchers, collaborators, and universities.

Do You Buy or Not? The Effect of Recommender Slogans on Retail Sites

Recommender systems solve the current information overload problem in the online world. By predicting and presenting relevant information, web users do not need to waste time searching and browsing for contents that they are interested in. However, in addition to the accurate contents, slogans associated catching the customers’ eyes are worthy of exploration. This study aims to discover the effects of various recommender slogans. Two categories of slogans were designed in the study: slogans associated with customer inputs and slogans associated with price promotion. Actual customers’ webpage clickstreams and purchase decisions were collected from a Taiwanese retail shopping Website. The effects of recommender slogans on product categories are different. Customers generally were drawn by the slogans associated with price promotion. This study brought to light the effects of different slogans on online shoppers. With the empirical findings, this study provides online retailers important guidelines regarding online customers’ behaviors towards the employment of recommender slogans.

Intelligent information recommendation algorithm under background of big data land cultivation

In order to solve the problem of serious information overload in the era of big data and improve the informatization of intelligent recommendation result, an intelligent information recommendation algorithm based on user preference mining was put forward. According to the background of big data, user behavior data is unified. The advantage of spark relative to compared Hadoop Map Reduce was analyzed through the operating architecture and upper ecosystem of spark, so that the data processing ability was improved. The user preference mining technology was integrated with the intelligent information recommendation algorithm. Moreover, the explicit user preference knowledge and implicit user preference knowledge were analyzed to obtain the user preference knowledge and the nearest neighbor community, and thus to complete the intelligent information recommendation. Experimental results show that the proposed algorithm can exactly reflect the user preferences in different groups, with good recommendation accuracy. In addition, the desired effect is achieved.

Profiling reviewers’ social network strength and predicting the “Helpfulness” of online customer reviews

Abstract Online customer reviews have become a popular source of information that influences the purchasing decisions of many prospective customers. However, the rapidly increasing volume of online reviews presents a problem of information overload, which makes it difficult for customers to determine the quality of the reviews. This study defines the helpfulness of the reviews as a count variable and takes the review helpfulness prediction from both regression and classification perspectives. The influence of friends and followers on review helpfulness is examined by introducing Social Network Strength (SNS) features. Furthermore, the performance of Machine Learning (ML) algorithms and the importance of features are separately examined for both problems using different time span of reviews. The evaluation performed using a dataset of 90,671 Yelp shopping reviews demonstrates the effectiveness of the proposed approach. The findings of this study have important theoretical and practical implications for researchers, businesses, reviewers and review platforms.

A Group Recommendation System of Network Document Resource Based on Knowledge Graph and LSTM in Edge Computing

The Internet has become one of the important channels for users to obtain information and knowledge. It is crucial to work out how to acquire personalized requirement of users accurately and effectively from huge amount of network document resources. Group recommendation is an information system for group participation in common activities that meets the common interests of all members in the group. This paper proposes a group recommendation system for network document resource exploration using the knowledge graph and LSTM in edge computing, which can solve the problem of information overload and resource trek effectively. An extensive system test has been carried out in the field of big data application in packaging industry. The experimental results show that the proposed system recommends network document resource more accurately and further improves recommendation quality using the knowledge graph and LSTM in edge computing. Therefore, it can meet the user’s personalized resource need more effectively.

Study of online learning resource recommendation based on improved BP neural network

Personalised recommendation has gradually become an effective way to solve the problem of information overload in the era of big data. Therefore, in order to improve the efficiency of online learning, this paper discusses the design of online learning resource recommendation algorithm based on improved BP neural network, and the results show that it has high value for popularisation and application. Based on the transmission network, the improved BP neural network of momentum factor can achieve more efficient data mining. After training learning resources and user data, it can match the real score and the predicted score, so as to ensure the accuracy of personalised recommendation. The main contribution of this paper is to propose a recommendation algorithm to online learning resources through improved BP neural network algorithm, and the feasibility of the algorithm is verified. The research method of this paper provides a reference for the research of personalised recommendation algorithm of online resources.

On the use of computer-assistance to facilitate systematic mapping.

The volume of published academic research is growing rapidly and this new era of "big literature" poses new challenges to evidence synthesis, pushing traditional, manual methods of evidence synthesis to their limits. New technology developments, including machine learning, are likely to provide solutions to the problem of information overload and allow scaling of systematic maps to large and even vast literatures. In this paper, we outline how systematic maps lend themselves well to automation and computer-assistance. We believe that it is a major priority to consolidate efforts to develop and validate efficient, rigorous and robust applications of these novel technologies, ensuring the challenges of big literature do not prevent the future production of systematic maps.

Adaptive query relaxation and result categorization of fuzzy spatiotemporal data based on XML

With the rapid development of spatiotemporal information and its applications, querying spatiotemporal data has received considerable attention. Meanwhile, the imprecision and uncertainty of information cannot be ignored in many practical applications. Querying fuzzy spatiotemporal data have become one of the most important topics in academia and industry. Although there have been some achievements in querying aspect, the study about fuzzy spatiotemporal query relaxation is still few. In fact, query relaxation is necessary when the amount of query results is small or even empty, especially in the process of querying fuzzy spatiotemporal data. In this paper, we propose an adaptive query relaxation and result categorization approach for fuzzy spatiotemporal data based on XML, which is compatible with spatiotemporal features when spatiotemporal related queries are performed. The approach does not depend on any specific domain or user, it can adaptively relax the initial query requirements, and classify the results by user context preferences and data distribution after query relaxation. In order to locate the spatiotemporal data quickly and show the corresponding fuzziness apparently, we adopted XML to construct the fuzzy spatiotemporal model for query relaxation because XML data can be represented as a tree model which can flexibly arrange the spatiotemporal nodes at the specified position and mark the fuzziness of nodes on the path. In addition, after query relaxation, we present the results categorization algorithm to address the problem of information overload, and then return a navigation tree to the user. Finally, we launch a comprehensive set of experiments to demonstrate the effectiveness and efficiency of our proposed approach. Results of experiments demonstrate that our adaptive query relaxation and result categorization approach based on XML has higher recall and precision in spatiotemporal related query, and can capture the user’s needs and preferences effectively as well.