How to Recommend by Online Lifestyle Tagging (OLT)

2014 ◽  
Vol 13 (06) ◽  
pp. 1183-1209 ◽  
Author(s):  
Yu Pan ◽  
Lijuan Luo ◽  
Dan Liu ◽  
Li Gao ◽  
Xiaobo Xu ◽  
...  
Keyword(s):  
User Behavior ◽  
Rapid Development ◽  
Personalized Recommendation ◽  
Chinese Consumers ◽  
Online Consumer Behavior ◽  
Marketing Tool ◽  
Online Lifestyle ◽  
Low Efficiency ◽  
Using Data ◽  
Fierce Competition

With the rapid development of the Internet, the online shopping market expands constantly. Inspired by fierce competition and complex and diverse consumer demand, personalized recommendation has become an effective marketing tool for e-commerce enterprises. However, the existing recommendation methods based on online consumer behavior or preferences are characterized by poor accuracy and low efficiency. The paper mainly conducts three studies, the study1 proves that seven online lifestyles, which are "Comfortable, Entertainment, Luxury, Tradition & Conservation, Rational, Fashion Sense, and Social Activities", affect Chinese consumers' purchase. However, the different online lifestyles have different effects on purchase, thus the response rates of recommending. The study2 proposes a new personalized recommendation method "online lifestyle tagging (OLT)" based on online lifestyle and user behavior tags to identify online lifestyles. In the study3, the efficiency of OLT is tested and verified using data collected from enterprises, it suggests that OLT has a significantly higher response rate than traditional behavior-based methods. This study demonstrates that OLT improves the accuracy and efficiency of personalized recommendation, and thus contributes to the theory of personalized recommendation and marketing methods based on lifestyle.

scholarly journals RESEARCH ON BEHAVIORAL MOTIVATION INFERENCE METHOD FOR GEOGRAPHICAL SPATIOTEMPORAL LARGE DATA

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 725-729
Author(s):  
J. W. Li ◽  
W. D. Chen ◽  
Y. Ma ◽  
N. Yu ◽  
X. Li ◽  
...  
Keyword(s):  
Big Data ◽  
Association Rules ◽  
User Behavior ◽  
Rapid Development ◽  
Frequent Itemsets ◽  
Internet Technology ◽  
Personalized Recommendation ◽  
User Interest ◽  
Geographic Context ◽  
Spatio Temporal

Abstract. Along with the rapid development of Internet technology, GNSS technology and mobile terminals, a large amount of information including geographical location and time attributes has been generated. Faced with large and complex Internet geospatial data, how to quickly and accurately extract valuable reference information becomes an urgent problem to be solved. And the user's demand for personalized information of recommendation information is getting stronger and stronger, and researching efficient and accurate personalized recommendation system has good application value. In this paper, based on the application requirements of personalized recommendation information, the GIS platform and related recommendation algorithms are used to fully exploit the user and location based on geographic space-time big dataIt is divided into user explicit interest and user implicit interest, and then establishes a scientific and efficient user behavior motivation prediction model based on geographic situation. User interest information can be obtained from explicit interest information, implicit interest information and geographic situation interest information. Geographical environment, geographic location and other related context information. By introducing time factors, it is used to update and improve the user real-time interest model to achieve accurate prediction of user behavior motives under geographic spatio-temporal big data. Use Apriori algorithm to calculate the support and determine the current Frequent itemsets of user interest in geographic context, using frequent itemsets to generate strong association rules, and realizing the analysis of user behavior motives based on geography context. For geographic spatio-temporal big data, this paper proposes a personalized hybrid recommendation algorithm, which is based on users. Effective combination of collaborative filtering algorithms and association rules for geographic context-user behavioral interest adaptation.

scholarly journals Penggunaan Media Sosial Instagram Haloa Cafe sebagai Komunikasi Pemasaran Online

2020 ◽  
Vol 10 (2) ◽  
pp. 177-190
Author(s):  
Nurul Rizky ◽  
Sri Dewi Setiawati
Keyword(s):  
Social Media ◽  
New Media ◽  
Marketing Strategy ◽  
Rapid Development ◽  
Qualitative Approach ◽  
Media Theory ◽  
Technological Advances ◽  
Marketing Tool ◽  
Fierce Competition

The rapid development of business today has resulted in increasingly fierce competition. Not only how a business provides products and services but how the products and services that have been produced can be accepted, known, by consumers. Marketing communications today can be done anywhere and anytime due to technological advances such as social media. In this study the researcher aimed to find out how the Haloa Cafe's marketing strategy through social media Instagram, in this study using the new media theory. The method used is a case study with a qualitative approach. The result of this research is that Haloa Cafe chooses social media Instagram as the main marketing tool in social media. This is attributed to the increasing number of Instagram social media users and in accordance with the marketing target of Haloa Café.

Research on the Impact of Information Security Certification and Concealment onFinancial Performance

2022 ◽  
Vol 30 (3) ◽  
pp. 0-0
Keyword(s):  
Information Security ◽  
Financial Performance ◽  
Positive Impact ◽  
Rapid Development ◽  
International Standards ◽  
Corporate Decisions ◽  
Using Data ◽  
The Impact ◽  
The Relationship ◽  
Iso 27001

With the rapid development of information technology, information security has been gaining attention. The International Organization for Standardization (ISO) has issued international standards and technical reports related to information security, which are gradually being adopted by enterprises. This study analyzes the relationship between information security certification (ISO 27001) and corporate financial performance using data from Chinese publicly listed companies. The study focusses on the impact of corporate decisions such as whether to obtain certification, how long to hold certification, and whether to publicize information regarding certification. The results show that there is a positive correlation between ISO 27001 and financial performance. Moreover, the positive impact of ISO 27001 on financial performance gradually increases with time. In addition, choosing not to publicize ISO 27001 certification can negatively affect enterprise performance.

scholarly journals Personalized Recommendation Model of High-Quality Education Resources for College Students Based on Data Mining

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chaohua Fang ◽  
Qiuyun Lu
Keyword(s):  
Learning Process ◽  
Data Science ◽  
Recommendation System ◽  
Information Overload ◽  
Rapid Development ◽  
Learning Ability ◽  
Personalized Recommendation ◽  
Learning Resources ◽  
Learner Model ◽  
Internet Education

With the rapid development of information technology and data science, as well as the innovative concept of “Internet+” education, personalized e-learning has received widespread attention in school education and family education. The development of education informatization has led to a rapid increase in the number of online learning users and an explosion in the number of learning resources, which makes learners face the dilemma of “information overload” and “learning lost” in the learning process. In the personalized learning resource recommendation system, the most critical thing is the construction of the learner model. Currently, most learner models generally have a lack of scientific focus that they have a single method of obtaining dimensions, feature attributes, and low computational complexity. These problems may lead to disagreement between the learner’s learning ability and the difficulty of the recommended learning resources and may lead to the cognitive overload or disorientation of learners in the learning process. The purpose of this paper is to construct a learner model to support the above problems and to strongly support individual learning resources recommendation by learning the resource model which effectively reduces the problem of cold start and sparsity in the recommended process. In this paper, we analyze the behavioral data of learners in the learning process and extract three features of learner’s cognitive ability, knowledge level, and preference for learning of learner model analysis. Among them, the preference model of the learner is constructed using the ontology, and the semantic relation between the knowledge is better understood, and the interest of the student learning is discovered.

Time, money, or convenience: what determines Chinese consumers’ continuance usage intention and behavior of using tourism mobile apps?

2019 ◽  
Vol 13 (3) ◽  
pp. 288-302 ◽  
Author(s):  
Feng Xu ◽  
Songshan (Sam) Huang ◽  
Shuaishuai Li
Keyword(s):  
User Behavior ◽  
Mobile Apps ◽  
Equation Modeling ◽  
Chinese Society ◽  
Chinese Consumers ◽  
Time Saving ◽  
Content Type ◽  
Continuance Usage ◽  
And Behavior ◽  
Continuance Usage Intention

Purpose This study aims to examine the effects of three aspects of perceived advantage (i.e. time-saving, money-saving and convenience) on Chinese consumers’ continuance usage intention and behavior of using tourism mobile applications (apps) in the context of Chinese society and culture. Design/methodology/approach Survey data were collected at 20 key tourist attractions in Jinan, China from tourists who visit the attractions. Structural equation modeling was applied to test the hypothetical model. Findings Empirical findings revealed that time-saving directly affected consumers’ continuance usage intention but did not influence user behavior; on the contrary, money-saving had a direct effect on user behavior, but not on intention. Convenience was found to affect both intention and behavior and had a much stronger total effect on user behavior than time-saving and money-saving. Research limitations/implications The study findings offer insights into the further development of tourism mobile apps. While money-saving can be an effective marketing offer for user adoption of tourism mobile apps, tourism mobile apps operators should further tap into the value of time and convenience in designing and developing tourism mobile apps. Originality/value The study expands on practical knowledge of Chinese consumers’ behavior toward using tourism apps.

The correlation analysis between the service quality of intelligent library and the behavioral intention of users

2019 ◽  
Vol 38 (1) ◽  
pp. 95-112 ◽  
Author(s):  
Min Chen ◽  
Chien-wen Shen
Keyword(s):  
Service Quality ◽  
Behavioral Intention ◽  
User Behavior ◽  
Rapid Development ◽  
Service Innovation ◽  
Relevant Information ◽  
Content Type ◽  
Positive Correlation ◽  
Service Mode

Purpose The purpose of this study was to explore the effect of innovative service mode of intelligent library on improving the service quality and a series of impacts on user behavior. With the rapid development of information technology, internet of things has become an important carrier of people’s “intelligent life”. The emergence of intelligent library will no longer be limited by space; it is affecting people’s lives and work imperceptibly. This new service mode was studied here, and the relationship between the service quality of intelligent library and users’ behavior was analyzed from the perspective of user acceptance and use behavior of intelligent library. Moreover, this study explores how to optimize the service quality to let users accept this technology and service mode and thus realize the original idea. Design/methodology/approach Through 800 questionnaires issued to the users in the Zhejiang Provincial AI Library, the authors obtained the study data. Among the received questionnaires, 676 copies are valid, and 124 responses are either incomplete or not answered, and so, the efficient rate is 84.5 per cent. Findings There is a significantly positive correlation between service innovation and service quality. There is a significantly positive correlation between service quality and behavioral intention. There is a significantly positive correlation between service innovation and behavioral intention. Originality/value From the point of view of innovative service, this paper analyzes the effect of innovative service mode of intelligent library on improving the service quality and a series of impacts on user behavior. This study confirms that intelligent library is a relatively new service innovation. Users’ curiosity and exploration will make them access some relevant information. As a result, a reasonably innovative service is an important factor in users’ acceptance behavior.

Cluster-Based Smartphone Predictive Analytics for Application Usage and Next Location Prediction

2018 ◽  
Vol 9 (2) ◽  
pp. 64-80
Author(s):  
Xiaoling Lu ◽  
Bharatendra Rai ◽  
Yan Zhong ◽  
Yuzhu Li
Keyword(s):  
Prediction Models ◽  
Predictive Analytics ◽  
User Behavior ◽  
Training Dataset ◽  
Location Prediction ◽  
Behavior Prediction ◽  
Test Dataset ◽  
Smartphone Sensors ◽  
Using Data ◽  
Prediction Approach

Prediction of app usage and location of smartphone users is an interesting problem and active area of research. Several smartphone sensors such as GPS, accelerometer, gyroscope, microphone, camera and Bluetooth make it easier to capture user behavior data and use it for appropriate analysis. However, differences in user behavior and increasing number of apps have made such prediction a challenging problem. In this article, a prediction approach that takes smartphone user behavior into consideration is proposed. The proposed approach is illustrated using data from over 30000 users from a leading IT company in China by first converting data in to recency, frequency, and monetary variables and then performing cluster analysis to capture user behavior. Prediction models are then developed for each cluster using a training dataset and their performance is assessed using a test dataset. The study involves ten different categories of apps and four different regions in Beijing. The proposed app usage prediction and next location prediction approach has provided interesting results.

scholarly journals Strong Brand From Consumers’ Perspective: A Cross-Cultural Study

2018 ◽  
Vol 50 (1) ◽  
pp. 116-129 ◽  
Author(s):  
Chenchen Li ◽  
Dongmei Li ◽  
Chi-Yue Chiu ◽  
Siqing Peng
Keyword(s):  
Cultural Differences ◽  
Personal Characteristics ◽  
The United States ◽  
Cross Cultural ◽  
Chinese Consumers ◽  
Brand Strength ◽  
Strength Evaluation ◽  
Cross Cultural Differences ◽  
Cross Cultural Study ◽  
Using Data

The present research investigates cross-cultural differences in the characteristics associated with brand strength evaluation and the mechanism underlying these cultural differences. Using data from the United States and China, we found that American consumers judge brands with personal characteristics to be stronger than those with relational characteristics, while Chinese consumers show a reversed pattern. Furthermore, cultural differences in brand strength evaluation were salient only when consumers rated brands that were connected with their self-concepts, suggesting that cultural differences in brand strength evaluation ensue from consumers’ internalized preferences. Our findings have theoretical and practical implications for branding management and understanding the mechanism through which culture influences individual behaviors.

scholarly journals Inhibition of histone deacetylase 1 (HDAC1) and HDAC2 enhances CRISPR/Cas9 genome editing

2019 ◽  
Vol 48 (2) ◽  
pp. 517-532 ◽  
Author(s):  
Bin Liu ◽  
Siwei Chen ◽  
Anouk La Rose ◽  
Deng Chen ◽  
Fangyuan Cao ◽  
...  
Keyword(s):  
Gene Editing ◽  
Gene Knockout ◽  
Rapid Development ◽  
Open Chromatin ◽  
Chromatin Compaction ◽  
Histone Deacetylase 1 ◽  
Cas9 Protein ◽  
Target Dna ◽  
Histone Modifiers ◽  
Low Efficiency

Abstract Despite the rapid development of CRISPR/Cas9-mediated gene editing technology, the gene editing potential of CRISPR/Cas9 is hampered by low efficiency, especially for clinical applications. One of the major challenges is that chromatin compaction inevitably limits the Cas9 protein access to the target DNA. However, chromatin compaction is precisely regulated by histone acetylation and deacetylation. To overcome these challenges, we have comprehensively assessed the impacts of histone modifiers such as HDAC (1–9) inhibitors and HAT (p300/CBP, Tip60 and MOZ) inhibitors, on CRISPR/Cas9 mediated gene editing efficiency. Our findings demonstrate that attenuation of HDAC1, HDAC2 activity, but not other HDACs, enhances CRISPR/Cas9-mediated gene knockout frequencies by NHEJ as well as gene knock-in by HDR. Conversely, inhibition of HDAC3 decreases gene editing frequencies. Furthermore, our study showed that attenuation of HDAC1, HDAC2 activity leads to an open chromatin state, facilitates Cas9 access and binding to the targeted DNA and increases the gene editing frequencies. This approach can be applied to other nucleases, such as ZFN and TALEN.

scholarly journals SVD++ Recommendation Algorithm Based on Backtracking

Information ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 369
Author(s):  
Shijie Wang ◽  
Guiling Sun ◽  
Yangyang Li
Keyword(s):  
Line Search ◽  
Optimal Solution ◽  
Recall Rate ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Backtracking Line Search ◽  
Low Efficiency ◽  
Public Datasets ◽  
Value Decomposition ◽  
Local Gradient

Collaborative filtering (CF) has successfully achieved application in personalized recommendation systems. The singular value decomposition (SVD)++ algorithm is employed as an optimized SVD algorithm to enhance the accuracy of prediction by generating implicit feedback. However, the SVD++ algorithm is limited primarily by its low efficiency of calculation in the recommendation. To address this limitation of the algorithm, this study proposes a novel method to accelerate the computation of the SVD++ algorithm, which can help achieve more accurate recommendation results. The core of the proposed method is to conduct a backtracking line search in the SVD++ algorithm, optimize the recommendation algorithm, and find the optimal solution via the backtracking line search on the local gradient of the objective function. The algorithm is compared with the conventional CF algorithm in the FilmTrust, MovieLens 1 M and 10 M public datasets. The effectiveness of the proposed method is demonstrated by comparing the root mean square error, absolute mean error and recall rate simulation results.

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