Discovery and classification of user interests on social media

2017 ◽  
Vol 45 (3) ◽  
pp. 130-138 ◽  
Author(s):  
Basit Shahzad ◽  
Ikramullah Lali ◽  
M. Saqib Nawaz ◽  
Waqar Aslam ◽  
Raza Mustafa ◽  
...  
Keyword(s):  
Information Filtering ◽  
Recommendation Systems ◽  
Personalized Recommendation ◽  
Support Vector ◽  
User Interest ◽  
Socioeconomic Impacts ◽  
Content Type ◽  
Positive Correlation ◽  
User Interests ◽  
Twitter Users

Purpose Twitter users’ generated data, known as tweets, are now not only used for communication and opinion sharing, but they are considered an important source of trendsetting, future prediction, recommendation systems and marketing. Using network features in tweet modeling and applying data mining and deep learning techniques on tweets is gaining more and more interest. Design/methodology/approach In this paper, user interests are discovered from Twitter Trends using a modeling approach that uses network-based text data (tweets). First, the popular trends are collected and stored in separate documents. These data are then pre-processed, followed by their labeling in respective categories. Data are then modeled and user interest for each Trending topic is calculated by considering positive tweets in that trend, average retweet and favorite count. Findings The proposed approach can be used to infer users’ topics of interest on Twitter and to categorize them. Support vector machine can be used for training and validation purposes. Positive tweets can be further analyzed to find user posting patterns. There is a positive correlation between tweets and Google data. Practical implications The results can be used in the development of information filtering and prediction systems, especially in personalized recommendation systems. Social implications Twitter microblogging platform offers content posting and sharing to billions of internet users worldwide. Therefore, this work has significant socioeconomic impacts. Originality/value This study guides on how Twitter network structure features can be exploited in discovering user interests using tweets. Further, positive correlation of Twitter Trends with Google Trends is reported, which validates the correctness of the authors’ approach.

A comparative analysis of Twitter users who Tweeted on psychology and political science journal articles

2019 ◽  
Vol 43 (7) ◽  
pp. 1188-1208
Author(s):  
Yanfen Zhou ◽  
Jin-Cheon Na
Keyword(s):  
Political Science ◽  
General Public ◽  
Support Vector ◽  
User Profiles ◽  
Journal Articles ◽  
Content Type ◽  
User Classification ◽  
Twitter User ◽  
Twitter Users ◽  
The Impact

Purpose The purpose of this paper is to understand the similarities and differences between the Twitter users who tweeted on journal articles in psychology and political science disciplines. Design/methodology/approach The data were collected from Web of Science, Altmetric.com, and Twitter. A total of 91,826 tweets with 22,541 distinct Twitter user profiles for psychology discipline and 29,958 tweets with 10,478 distinct Twitter user profiles for political science discipline were used for analysis. The demographics analysis includes gender, geographic location, individual or organization user, academic or non-academic background, and psychology/political science domain knowledge background. A machine learning approach using support vector machine (SVM) was used for user classification based on the Twitter user profile information. Latent Dirichlet allocation (LDA) topic modeling was used to discover the topics that the users discussed from the tweets. Findings Results showed that the demographics of Twitter users who tweeted on psychology and political science are significantly different. Tweets on journal articles in psychology reflected more the impact of scientific research finding on the general public and attracted more attention from the general public than the ones in political science. Disciplinary difference in term of user demographics exists, and thus it is important to take the discipline into consideration for future altmetrics studies. Originality/value From this study, researchers or research organizations may have a better idea on who their audiences are, and hence more effective strategies can be taken by researchers or organizations to reach a wider audience and enhance their influence.

A review of a recommendation filtering system approach based on reliable sustainable opinion mining

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Imen Gmach ◽  
Nadia Abaoub ◽  
Rubina Khan ◽  
Naoufel Mahfoudh ◽  
Amira Kaddour
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Information Search ◽  
Recommendation System ◽  
Opinion Mining ◽  
Information Filtering ◽  
Recommendation Systems ◽  
Content Filtering ◽  
Content Type ◽  
The Impact

PurposeIn this article the authors will focus on the state of the art on information filtering and recommender systems based on trust. Then the authors will represent a variety of filtering and recommendation techniques studied in different literature, like basic content filtering, collaborative filtering and hybrid filtering. The authors will also examine different trust-based recommendation algorithms. It will ends with a summary of the different existing approaches and it develops the link between trust, sustainability and recommender systems.Design/methodology/approachMethodology of this study will begin with a general introduction to the different approaches of recommendation systems; then define trust and its relationship with recommender systems. At the end the authors will present their approach to “trust-based recommendation systems”.FindingsThe purpose of this study is to understand how groups of users could improve trust in a recommendation system. The authors will examine how to evaluate the performance of recommender systems to ensure their ability to meet the needs that led to its creation and to make the system sustainable with respect to the information. The authors know very well that selecting a measure must depend on the type of data to be processed and user interests. Since the recommendation domain is derived from information search paradigms, it is obvious to use the evaluation measures of information systems.Originality/valueThe authors presented a list of recommendations systems. They examined and compared several recommendation approaches. The authors then analyzed the dominance of collaborative filtering in the field and the emergence of Recommender Systems in social web. Then the authors presented and analyzed different trust algorithms. Finally, their proposal was to measure the impact of trust in recommendation systems.

Finding m-similar users in social networks using the m-representative skyline query

2017 ◽  
Vol 45 (3) ◽  
pp. 121-129
Author(s):  
Kuo-Cheng Ting ◽  
Ruei-Ping Wang ◽  
Yi-Chung Chen ◽  
Don-Lin Yang ◽  
Hsi-Min Chen
Keyword(s):  
Social Networks ◽  
Recommendation Systems ◽  
Personalized Recommendation ◽  
Skyline Query ◽  
Skyline Queries ◽  
Content Type ◽  
Target User ◽  
User Data ◽  
Treat All ◽  
Representative Skyline

Purpose Using social networks to identify users with traits similar to those of the target user has proven highly effective in the development of personalized recommendation systems. Existing methods treat all dimensions of user data as a whole, despite the fact that most of the information related to different dimensions is discrete. This has prompted researchers to adopt the skyline query for such search functions. Unfortunately, researchers have run into problems of instability in the number of users identified using this approach. Design/methodology/approach We thus propose the m-representative skyline queries to provide control over the number of similar users that are returned. We also developed an R-tree-based algorithm to implement the m-representative skyline queries. Findings By using the R-tree based algorithm, the processing speed of the m-representative skyline queries can now be accelerated. Experiment results demonstrate the efficacy of the proposed approach. Originality/value Note that with this new way of finding similar users in the social network, the performance of the personalized recommendation systems is expected to be enhanced.

scholarly journals RESEARCH ON PERSONALIZED RECOMMENDATION ALGORITHM FUSING TIME AND LOCATION

CONVERTER ◽  
2021 ◽  
pp. 302-314
Author(s):  
Zhongyong Fan, Yongqian Zhao, Yongkang Wang, Zhijun Zhang
Keyword(s):  
Recommendation Systems ◽  
Geographic Location ◽  
Personalized Recommendation ◽  
Location Information ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
User Interests ◽  
Dependent Change ◽  
Collaborative Filtering Algorithm ◽  
Time Dependent Change

With development of recommendation systems, they are faced with more and more challenges. In order to relieve problems existing in commodity selection by users of different preferences from different regions, personalized recommendation based on location information has emerged. Nowadays most recommendation systems based on location information neglect the fact that users’ preference will change with time. To solve the above problem, geographic location and time factor of users are effectively combined in this paper, and a personalized recommendation algorithm TLPR combining time and location information is proposed. This algorithm determines the users’ geographic location according to postcode information of the users, uses pyramid quadtree model to distribute users into nodes at each layer in the pyramid, utilizes collaborative filtering algorithm for local recommendation in each node, introduces a time function to regulate time-dependent change of user interests when calculating user similarity at each node and finally realizes a comprehensive recommendation by distributing a weight for recommendation result at each layer in the pyramid quadtree. A comparative experience is carried out for recommendation performance of this algorithm on MovieLens dataset, and experimental results indicate that this algorithm is of better recommendation effect

scholarly journals Efficient Mining of Interest Patterns on Click Stream Data

2019 ◽  
Vol 9 (2) ◽  
pp. 635-639
Keyword(s):  
Personalized Recommendation ◽  
Web Pages ◽  
Stream Data ◽  
User Interest ◽  
Time Duration ◽  
Online Data ◽  
User Interests ◽  
Tremendous Amount ◽  
Navigation Patterns ◽  
The Web

Nowadays, large amount of data is generated daily in e-commerce applications as click stream data. Because of the availability of this tremendous amount of data analyzing the user browsing behaviour and finding frequent navigation patterns of different web pages accessed by web users is an key element for retailers to optimize the website and personalized the web services of different e-commerce websites. User browsing behaviour is evaluated based on user interests on web pages or products. There are different parameters are considered while analyzing the click stream data for calculating frequent navigation patterns and context based customer behaviour in online data bases. In this paper we developed different models for optimizing and personalizing web service and sequential frequent patterns using the parameters: browsing path, frequently visited web pages, time duration of web pages and user interest. These novel models uses the parameters and applied on click stream data to optimize the web pages and improve the personalized recommendation.

What books will be your bestseller? A machine learning approach with Amazon Kindle

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Seungpeel Lee ◽  
Honggeun Ji ◽  
Jina Kim ◽  
Eunil Park
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
User Satisfaction ◽  
Large Scale ◽  
Recommendation Systems ◽  
Feature Representation ◽  
Support Vector ◽  
Data Set ◽  
Content Type ◽  
Online Stores

Purpose With the rapid increase in internet use, most people tend to purchase books through online stores. Several such stores also provide book recommendations for buyer convenience, and both collaborative and content-based filtering approaches have been widely used for building these recommendation systems. However, both approaches have significant limitations, including cold start and data sparsity. To overcome these limitations, this study aims to investigate whether user satisfaction can be predicted based on easily accessible book descriptions. Design/methodology/approach The authors collected a large-scale Kindle Books data set containing book descriptions and ratings, and calculated whether a specific book will receive a high rating. For this purpose, several feature representation methods (bag-of-words, term frequency–inverse document frequency [TF-IDF] and Word2vec) and machine learning classifiers (logistic regression, random forest, naive Bayes and support vector machine) were used. Findings The used classifiers show substantial accuracy in predicting reader satisfaction. Among them, the random forest classifier combined with the TF-IDF feature representation method exhibited the highest accuracy at 96.09%. Originality/value This study revealed that user satisfaction can be predicted based on book descriptions and shed light on the limitations of existing recommendation systems. Further, both practical and theoretical implications have been discussed.

A novel sensor array and classifier optimization method of electronic nose based on enhanced quantum-behaved particle swarm optimization

Sensor Review ◽  
2014 ◽  
Vol 34 (3) ◽  
pp. 304-311 ◽  
Author(s):  
Pengfei Jia ◽  
Fengchun Tian ◽  
Shu Fan ◽  
Qinghua He ◽  
Jingwei Feng ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Wound Infection ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Sensor Array ◽  
Particle Swarm ◽  
Optimization Method ◽  
Support Vector ◽  
Swarm Optimization ◽  
Content Type

Purpose – The purpose of the paper is to propose a new optimization algorithm to realize a synchronous optimization of sensor array and classifier, to improve the performance of E-nose in the detection of wound infection. When an electronic nose (E-nose) is used to detect the wound infection, sensor array’s optimization and parameters’ setting of classifier have a strong impact on the classification accuracy. Design/methodology/approach – An enhanced quantum-behaved particle swarm optimization based on genetic algorithm, genetic quantum-behaved particle swarm optimization (G-QPSO), is proposed to realize a synchronous optimization of sensor array and classifier. The importance-factor (I-F) method is used to weight the sensors of E-nose by its degree of importance in classification. Both radical basis function network and support vector machine are used for classification. Findings – The classification accuracy of E-nose is the highest when the weighting coefficients of the I-F method and classifier’s parameters are optimized by G-QPSO. All results make it clear that the proposed method is an ideal optimization method of E-nose in the detection of wound infection. Research limitations/implications – To make the proposed optimization method more effective, the key point of further research is to enhance the classifier of E-nose. Practical implications – In this paper, E-nose is used to distinguish the class of wound infection; meanwhile, G-QPSO is used to realize a synchronous optimization of sensor array and classifier of E-nose. These are all important for E-nose to realize its clinical application in wound monitoring. Originality/value – The innovative concept improves the performance of E-nose in wound monitoring and paves the way for the clinical detection of E-nose.

Relationship amongst ResearchGate altmetric indicators and Scopus bibliometric indicators

2015 ◽  
Vol 116 (9/10) ◽  
pp. 564-577 ◽  
Author(s):  
RISHABH SHRIVASTAVA ◽  
Preeti Mahajan
Keyword(s):  
Sample Size ◽  
Correlation Coefficients ◽  
Strong Positive Correlation ◽  
Bibliometric Indicators ◽  
Pearson’S Correlation ◽  
Content Type ◽  
Positive Correlation ◽  
Pearson's Correlation ◽  
Different Characteristics ◽  
The Relationship

Purpose – The purpose of this paper is twofold. First, the study aims to investigate the relationship between the altmetric indicators from ResearchGate (RG) and the bibliometric indicators from the Scopus database. Second, the study seeks to examine the relationship amongst the RG altmetric indicators themselves. RG is a rich source of altmetric indicators such as Citations, RGScore, Impact Points, Profile Views, Publication Views, etc. Design/methodology/approach – For establishing whether RG metrics showed the same results as the established sources of metrics, Pearson’s correlation coefficients were calculated between the metrics provided by RG and the metrics obtained from Scopus. Pearson’s correlation coefficients were also calculated for the metrics provided by RG. The data were collected by visiting the profile pages of all the members who had an account in RG under the Department of Physics, Panjab University, Chandigarh (India). Findings – The study showed that most of the RG metrics showed strong positive correlation with the Scopus metrics, except for RGScore (RG) and Citations (Scopus), which showed moderate positive correlation. It was also found that the RG metrics showed moderate to strong positive correlation amongst each other. Research limitations/implications – The limitation of this study is that more and more scientists and researchers may join RG in the future, therefore the data may change. The study focuses on the members who had an account in RG under the Department of Physics, Panjab University, Chandigarh (India). Perhaps further studies can be conducted by increasing the sample size and by taking a different sample size having different characteristics. Originality/value – Being an emerging field, not much has been conducted in the area of altmetrics. Very few studies have been conducted on the reach of academic social networks like RG and their validity as sources of altmetric indicators like RGScore, Impact Points, etc. The findings offer insights to the question whether RG can be used as an alternative to traditional sources of bibliometric indicators, especially with reference to a rapidly developing country such as India.

A Collaborative Filtering Recommendation Algorithm Incorporated with Life Cycle

2013 ◽  
Vol 765-767 ◽  
pp. 630-633 ◽  
Author(s):  
Chong Lin Zheng ◽  
Kuang Rong Hao ◽  
Yong Sheng Ding
Keyword(s):  
Life Cycle ◽  
Collaborative Filtering ◽  
Recommendation Systems ◽  
Experimental Results ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Time Information ◽  
Successful Technology

Collaborative filtering recommendation algorithm is the most successful technology for recommendation systems. However, traditional collaborative filtering recommendation algorithm does not consider the change of time information. For this problem,this paper improve the algorithm with two new methods:Predict score incorporated with time information in order to reflect the user interest change; Recommend according to scores by adding the weight information determined by the item life cycle. Experimental results show that the proposed algorithm outperforms the traditional item in accuracy.

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