e-Shop User Preferences via User Behavior

2014 ◽  
Author(s):  
Peter Ojtáš ◽  
Ladislav Peška
Keyword(s):  
User Behavior ◽  
User Preferences

scholarly journals A Prediction Method of Mobile User Preference Based on the Influence between Users

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yancui Shi ◽  
Jianhua Cao ◽  
Congcong Xiong ◽  
Xiankun Zhang
Keyword(s):  
Social Network ◽  
User Behavior ◽  
Prediction Method ◽  
Mobile User ◽  
User Preferences ◽  
User Preference ◽  
Context Information ◽  
Mobile Social Network ◽  
Data Set ◽  
Interaction Behavior

User preference will be impacted by other users. To accurately predict mobile user preference, the influence between users is introduced into the prediction model of user preference. First, the mobile social network is constructed according to the interaction behavior of the mobile user, and the influence of the user is calculated according to the topology of the constructed mobile social network and mobile user behavior. Second, the influence between users is calculated according to the user’s influence, the interaction behavior between users, and the similarity of user preferences. When calculating the influence based on the interaction behavior, the context information is considered; the context information and the order of user preferences are considered when calculating the influence based on the similarity of user preferences. The improved collaborative filtering method is then employed to predict mobile user preferences based on the obtained influence between users. Finally, the experiment is executed on the real data set and the integrated data set, and the results show that the proposed method can obtain more accurate mobile user preferences than those of existing methods.

scholarly journals Integration of Museum User Behavior Information Based on Wireless Network

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Shuangji Liu ◽  
Yongzhong Yang ◽  
Yiwei Wang
Keyword(s):  
Wireless Network ◽  
User Behavior ◽  
Decay Rates ◽  
User Preferences ◽  
Information Resources ◽  
Information Resource ◽  
User Interest ◽  
Feature Vectors ◽  
Museum Information ◽  
Cultural Relics

Online museum information resource systems are getting popular these days which allow the users to get detailed information about the objects of their interest, and the user preferences are stored to search for related artifacts considering his/her online behavior. The behavior of users browsing online is integrated to capture relevant information which is integrated into museum information resources. Unfortunately, present implementations have errors in integration and optimization system, so a wireless network-based museum user behavior information integration system is proposed to calculate the user’s interest in museum’s cultural relics. The user behavior information resource model is developed based upon the degree of user interest, and forgetting functions with different decay rates are employed to describe changes in the interest level. This information is then used to construct users’ interest matrices. This matrix also contains information regarding the cultural relics that users have not yet visited. The system will introduce the interest weights of feature words to take the top features of the user behavior information for the integration of the users’ behavior and to combine the feature vectors that can represent the overall trajectory. Moreover, those feature vectors are described that can represent the local trajectory into feature vector to identify the slow-moving sparse targets, which is then utilized for the integration of users’ behavior information. The simulation tests prove that the proposed method can achieve low error in the integration process of user behavior information resources, thereby yielding good results.

A Dynamic Recommender System with Fused Time and Location Factors

2019 ◽  
Vol 23 (1) ◽  
pp. 25-33
Author(s):  
Xinhua Wang ◽  
Peng Yin ◽  
Yukai Gao ◽  
Lei Guo ◽  
◽  
...  
Keyword(s):  
Recommender System ◽  
User Behavior ◽  
Geographical Location ◽  
User Preferences ◽  
Dynamic Features ◽  
Fusion Model ◽  
User Interest ◽  
Location Factors ◽  
User Behavior Analysis ◽  
Real World Datasets

A recommender system is an important tool to help users obtain content and overcome information overload. It can predict users’ interests and offer recommendations by analyzing their history behaviors. However, traditional recommender systems focus primarily on static user behavior analysis. Recently, with the promotion of the Netflix recommendation prize and the open dataset with location and time information, many researchers have focused on the dynamic characteristics of the recommender system (including the changes in the dynamic model of user interest), and begun to offer recommendations based on these dynamic features. Intuitively, these dynamic user features provide us with an effective method to learn user interests deeply. Based on the observations above, we present a dynamic fusion model by integrating geographical location, user preferences, and the time factor based on the Gibbs sampling process to provide better recommendations. To evaluate the performance of our proposed method, we conducted experiments on real-world datasets. The experimental results indicate that our proposed dynamic recommender system with fused time and location factors not only performs well in traditional scenarios, but also in sparsity situations where users appear at the first time.

scholarly journals Graph Neural Network and Context-Aware Based User Behavior Prediction and Recommendation System Research

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Qian Gao ◽  
Pengcheng Ma
Keyword(s):  
Neural Network ◽  
Recommendation System ◽  
User Behavior ◽  
Absolute Error ◽  
User Preferences ◽  
Feature Interaction ◽  
Context Aware ◽  
Behavior Prediction ◽  
Complex Interactions ◽  
System Maps

Due to the influence of context information on user behavior, context-aware recommendation system (CARS) has attracted extensive attention in recent years. The most advanced context-aware recommendation system maps the original multi-field features into a shared hidden space and then simply connects it to a deep neural network (DNN) or other specially designed networks. However, for different areas, the ability of modeling complex interactions in a sufficiently flexible and explicit way is limited by the simple unstructured combination of feature fields. Therefore, it is hard to get the accurate results of the user behavior prediction. In this paper, a graph structure is used to establish the interaction between context and users/items. Through modeling user behavior, we can explore user preferences in different context environments, so as to make personalized recommendations for users. In particular, we construct a context-user and context-item interactions graph separately. In the interactions graph, each node is composed of a user feature field, an item feature field, and a feature field of different contexts. Different feature fields can interact through edges. Therefore, the task of modeling feature interaction can be transformed into modeling the node interaction on the corresponding graph. To this end, an innovative model called context-aware graph neural network (CA-GNN) model is designed. Furthermore, in order to obtain more accurate and efficient recommendation results, first, we innovatively use the attention mechanism to improve the interpretability of CA-GNN; second, we innovatively use the degree of physical fatigue features which has never been used in traditional CARS as critical contextual feature information into our CA-GNN. We simulated the Food and Yelp datasets. The experimental results show that CA-GNN is better than other methods in terms of root mean square error (RMSE) and mean absolute error (MAE).

scholarly journals Neural Framework for Joint Evolution Modeling of User Feedback and Social Links in Dynamic Social Networks

2018 ◽  
Author(s):  
Peizhi Wu ◽  
Yi Tu ◽  
Xiaojie Yuan ◽  
Adam Jatowt ◽  
Zhenglu Yang
Keyword(s):  
Social Networks ◽  
User Behavior ◽  
Optimal Strategies ◽  
User Feedback ◽  
User Preferences ◽  
User Behaviors ◽  
Dynamic Social Networks ◽  
Real World Datasets ◽  
Dynamic Attributes ◽  
Evolution Modeling

Modeling the evolution of user feedback and social links in dynamic social networks is of considerable significance, because it is the basis of many applications, including recommendation systems and user behavior analyses. Most of the existing methods in this area model user behaviors separately and consider only certain aspects of this problem, such as dynamic preferences of users, dynamic attributes of items, evolutions of social networks, and their partial integration. This work proposes a comprehensive general neural framework with several optimal strategies to jointly model the evolution of user feedback and social links. The framework considers the dynamic user preferences, dynamic item attributes, and time-dependent social links in time evolving social networks. Experimental results conducted on two real-world datasets demonstrate that our proposed model performs remarkably better than state-of-the-art methods.

scholarly journals WHY MAKING MATTERS ONLINE: THE PINTEREST-DIY DATA SET

2021 ◽  
Author(s):  
Michelle Sylvia Weintraub ◽  
David R W Sears
Keyword(s):  
User Behavior ◽  
Real Life ◽  
User Preferences ◽  
Social Networking Service ◽  
Text Data ◽  
Data Set ◽  
Creative Processes ◽  
User Comments ◽  
Lead Users ◽  
Do It Yourself

ABSTRACT The Do-It-Yourself (DIY) community is currently one of the largest creative content communities on Pinterest (Hall et al., 2018), a social networking service (SNS) that encourages users to both share information about creative processes and attempt projects in real life (IRL). Pinterest users share ongoing projects by creating Project “Pins”, which consist of images, videos, and text descriptions of creative content. And yet, while several studies have investigated user behavior in relation to everyday ideation and creativity on the site (Linder et al., 2014, Hu et al., 2018, Mull and Lee, 2014), little is known about the characteristics that lead users to prefer some DIY projects over others. Thus, this paper introduces the Pinterest-DIY data set, which consists of text data mined from 500 DIY project Pins on Pinterest. Using a custom sampling approach, we created a taxonomy of DIY characteristics related to each Pin’s project type, function, materials, and complexity. To measure user preferences on the site, we also conducted a sentiment analysis on user comments for each DIY project Pin. This paper introduces the data set and presents two use cases for the internet research community using both exploratory and confirmatory statistical methods. In our view, the Pinterest-DIY data set will provide further opportunities to examine whether, and to what degree, participation in online DIY communities promotes everyday creativity and increases engagement with physical matter.

scholarly journals Exploiting the User Social Context to Address Neighborhood Bias in Collaborative Filtering Music Recommender Systems

Information ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 439 ◽  
Author(s):  
Diego Sánchez-Moreno ◽  
Vivian López Batista ◽  
M. Dolores Muñoz Vicente ◽  
Ángel Luis Sánchez Lázaro ◽  
María N. Moreno-García
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
User Behavior ◽  
Error Rates ◽  
User Preferences ◽  
Music Recommendation ◽  
Power Law Distribution ◽  
Long Tail ◽  
Music Recommender Systems ◽  
Structure Metrics

Recent research in the field of recommender systems focuses on the incorporation of social information into collaborative filtering methods to improve the reliability of recommendations. Social networks enclose valuable data regarding user behavior and connections that can be exploited in this area to infer knowledge about user preferences and social influence. The fact that streaming music platforms have some social functionalities also allows this type of information to be used for music recommendation. In this work, we take advantage of the friendship structure to address a type of recommendation bias derived from the way collaborative filtering methods compute the neighborhood. These methods restrict the rating predictions for a user to the items that have been rated by their nearest neighbors while leaving out other items that might be of his/her interest. This problem is different from the popularity bias caused by the power-law distribution of the item rating frequency (long-tail), well-known in the music domain, although both shortcomings can be related. Our proposal is based on extending and diversifying the neighborhood by capturing trust and homophily effects between users through social structure metrics. The results show an increase in potentially recommendable items while reducing recommendation error rates.

scholarly journals Subjective and Objective User Behavior Disparity: Towards Balanced Visual Design and Color Adjustment

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8502
Author(s):  
Anna Lewandowska ◽  
Agnieszka Olejnik-Krugly ◽  
Jarosław Jankowski ◽  
Malwina Dziśko
Keyword(s):  
User Experience ◽  
User Behavior ◽  
User Preferences ◽  
Eye Tracker ◽  
User Friendliness ◽  
Visual Objects ◽  
Trade Offs ◽  
User Attention ◽  
Accessibility Guidelines ◽  
Color Combinations

Interactive environments create endless possibilities for the design of websites, games, online platforms, and mobile applications. Their visual aspects and functional characteristics influence the user experience. Depending on the project, the purpose of the environment can be oriented toward marketing targets, user experience, or accessibility. Often, these conflicting aspects should be integrated within a single project, and a search for trade-offs is needed. One of these conflicts involves a disparity in user behavior concerning declared preferences and real observed activity in terms of visual attention. Taking into account accessibility guidelines (WCAG) further complicates the problem. In our study, we focused on the analysis of color combinations and their contrast in terms of user-friendliness; visual intensity, which is important for attracting user attention; and recommendations from the Web Accessibility Guidelines (WCAG). We took up the challenge to reduce the disparity between user preferences and WCAG contrast, on one hand, and user natural behavior registered with an eye-tracker, on the other. However, we left the choice of what is more important—human conscious reaction or objective user behavior results—to the designer. The former corresponds to user-friendliness, while the latter, visual intensity, is consistent with marketing expectations. The results show that the ranking of visual objects characterized by different levels of contrast differs when considering the perspectives of user experience, commercial goals, and objective recording. We also propose an interactive tool with the possibility of assigning weights to each criterion to generate a ranking of objects.

scholarly journals Imparting Gestalt Essence in Identifying User Preferences towards Interior Design

2020 ◽  
Vol 5 (1) ◽  
pp. 25-33
Author(s):  
Nur Syatirah Othman ◽  
Mohd Nizam Osman ◽  
Nor Arzami Othman
Keyword(s):  
Interior Design ◽  
User Behavior ◽  
Three Dimensional ◽  
User Acceptance ◽  
User Preferences ◽  
Multimedia Application ◽  
Acceptance Test ◽  
Usability Problems ◽  
Use Of Technology ◽  
Target User

We believe that interior design has becoming more popular in this century nowadays. The purpose of this study is to design and develop multimedia application that can identify human behavior and preferences in interior design. Alessi and Trollip Instructional Design Model has been utilized as a methodology in this study which consist of planning, design and development. Heuristic Evaluation and User Acceptance Test has been applied in completing this experiment. Three multimedia experts selected randomly to identify usability problems that occur in the user interface (UI) design. After the refinement was made to the application, the User Acceptance Test was conducted to the user. A total of 60 participants at random selected from certain district of Perlis and Langkawi as a target user to participate in this study. The results demonstrate this multimedia application is effective in satisfying the user needs and demand of the decoration of their dream house. Thus, the researcher was able to identify user behavior and preferences in interior design. With three dimensional (3D) features that were applied in this multimedia application, it helps the user to feel more self-assured with their interior design. At the end of this research, the development of this application bring numerous benefits for both parties either the users or the developer in many aspects. Thus, the society will be disclosing to the use of technology in the interior design in this sophisticated era.

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