An entertainment recommendation system using the dynamics of user behavior over time

2014 ◽  
Author(s):  
Sajal Halder ◽  
Md. Hanif Seddiqui ◽  
Young-Koo Lee
Keyword(s):  
Recommendation System ◽  
User Behavior ◽  
Over Time

scholarly journals A recommendation system for scientific water data

2021 ◽  
Author(s):  
Zhaokun Xue ◽  
Alva Couch
Keyword(s):  
Data Sharing ◽  
Topic Modeling ◽  
Recommendation System ◽  
Latent Dirichlet Allocation ◽  
User Behavior ◽  
Change Over Time ◽  
Task Oriented ◽  
Content Based Filtering ◽  
Over Time

AbstractWe describe a recommendation system for HydroShare, a platform for scientific water data sharing. We discuss similarities, differences and challenges for implementing recommendation systems for scientific water data sharing. We discuss and analyze the behaviors that scientists exhibit in using HydroShare as documented by users’ activity logs. Unlike entertainment system users, users on HydroShare tend to be task-oriented, where the set of tasks of interest can change over time, and older interests are sometimes no longer relevant. By validating recommendation approaches against user behavior as expressed in activity logs, we conclude that a combination of content-based filtering and a latent Dirichlet allocation (LDA) topic modeling of user behavior—rather than and instead of LDA classification of dataset topics—provides a workable solution for HydroShare and compares this approach to existing recommendation methods.

scholarly journals User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Triyanna Widiyaningtyas ◽  
Indriana Hidayah ◽  
Teguh B. Adji
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
User Behavior ◽  
Correlation Coefficients ◽  
User Profile ◽  
Profile Data ◽  
Similarity Algorithm ◽  
Previous Algorithm ◽  
Movie Recommendation ◽  
Recommendation Accuracy

AbstractCollaborative filtering is one of the most widely used recommendation system approaches. One issue in collaborative filtering is how to use a similarity algorithm to increase the accuracy of the recommendation system. Most recently, a similarity algorithm that combines the user rating value and the user behavior value has been proposed. The user behavior value is obtained from the user score probability in assessing the genre data. The problem with the algorithm is it only considers genre data for capturing user behavior value. Therefore, this study proposes a new similarity algorithm – so-called User Profile Correlation-based Similarity (UPCSim) – that examines the genre data and the user profile data, namely age, gender, occupation, and location. All the user profile data are used to find the weights of the similarities of user rating value and user behavior value. The weights of both similarities are obtained by calculating the correlation coefficients between the user profile data and the user rating or behavior values. An experiment shows that the UPCSim algorithm outperforms the previous algorithm on recommendation accuracy, reducing MAE by 1.64% and RMSE by 1.4%.

scholarly journals PRUB: A Privacy Protection Friend Recommendation System Based on User Behavior

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Jiang ◽  
Ruijin Wang ◽  
Zhiyuan Xu ◽  
Yaodong Huang ◽  
Shuo Chang ◽  
...  
Keyword(s):  
Social Network ◽  
Recommendation System ◽  
User Behavior ◽  
Network Services ◽  
Mobile Social Network ◽  
Fine Grained ◽  
Friend Recommendation ◽  
Social Applications ◽  
Browsing Behavior ◽  
Behavioral Information

The fast developing social network is a double-edged sword. It remains a serious problem to provide users with excellent mobile social network services as well as protecting privacy data. Most popular social applications utilize behavior of users to build connection with people having similar behavior, thus improving user experience. However, many users do not want to share their certain behavioral information to the recommendation system. In this paper, we aim to design a secure friend recommendation system based on the user behavior, called PRUB. The system proposed aims at achieving fine-grained recommendation to friends who share some same characteristics without exposing the actual user behavior. We utilized the anonymous data from a Chinese ISP, which records the user browsing behavior, for 3 months to test our system. The experiment result shows that our system can achieve a remarkable recommendation goal and, at the same time, protect the privacy of the user behavior information.

Perbandingan Interpretasi Analisis Data Page View dan Click Related Post Pada Website Edukasi Sebagai Implisit Feedback Dengan Hasil Interpretasi Penggunaan Teknologi EyeTracking

2021 ◽  
Vol 3 (2) ◽  
pp. 66-72
Author(s):  
Riad Taufik Lazwardi ◽  
Khoirul Umam
Keyword(s):  
Decision Process ◽  
Recommendation System ◽  
User Behavior ◽  
Implicit Feedback ◽  
Decision Making Process ◽  
Learning Material ◽  
Relative Preferences ◽  
Insight Into ◽  
Explicit Feedback ◽  
Data Page

The analysis used in this study uses the help of Google Analytics to understand how the user's behavior on the Calculus learning material educational website page. Are users interested in recommendation articles? The answer to this question provides insight into the user's decision process and suggests how far a click is the result of an informed decision. Based on these results, it is hoped that a strategy to generate feedback from clicks should emerge. To evaluate the extent to which feedback shows relevance, versus implicit feedback to explicit feedback collected manually. The study presented in this study differs in at least two ways from previous work assessing the reliability of implicit feedback. First, this study aims to provide detailed insight into the user decision-making process through the use of a recommendation system with an implicit feedback feature. Second, evaluate the relative preferences that come from user behavior (user behavior). This differs from previous studies which primarily assessed absolute feedback. 

An Air Force Pilot Training Recommendation System Using Advanced Analytical Methods

2021 ◽  
Author(s):  
Nicolas C. Forrest ◽  
Raymond R. Hill ◽  
Phillip R. Jenkins
Keyword(s):  
Deep Learning ◽  
Training Program ◽  
Air Force ◽  
Analytical Methods ◽  
Recommendation System ◽  
Training Programs ◽  
Pilot Training ◽  
Over Time

The planning of individualized pilot training programs is an intensive process. Over 120 maneuvers are introduced into the training program over time while ensuring maneuver competencies. This work introduces a novel, deep-learning based approach for automatically generating training plans for pilot trainees to significantly reduce instructor pilot planning requirements.

Introduction

2021 ◽  
pp. 1-22
Author(s):  
Karen Mossberger ◽  
Eric W. Welch ◽  
Yonghong Wu
Keyword(s):  
Internet Use ◽  
User Behavior ◽  
Evaluation Research ◽  
Black Box ◽  
Change Over Time ◽  
Broadband Internet ◽  
Transformative Potential ◽  
Over Time

Broadband internet use is often heralded for its transformative potential in a broad range of policy areas, but there is scarce evidence on whether this is so, and how it can be utilized most effectively by organizations and communities. While the attribution of change to programmatic efforts is a familiar challenge in evaluation research, broadband technologies present some particular issues for evaluation: the “black box” problem of understanding user behavior, the complexity of theorizing about the interaction between technology and policy-specific processes, and understanding change over time. How can we better address both the challenges and the opportunities for evaluating broadband initiatives? This chapter introduces the plan of the volume in the context of answering these questions.

DSS for Web Mining Using Recommendation System

2017 ◽  
pp. 22-34 ◽  
Author(s):  
Varaprasad Rao M ◽  
Vishnu Murthy G
Keyword(s):  
Recommender Systems ◽  
Web Mining ◽  
Recommendation System ◽  
User Behavior ◽  
Web Content ◽  
Web Structure ◽  
Web Structure Mining ◽  
Content Mining ◽  
Computer Based ◽  
The Many

Decision Supports Systems (DSS) are computer-based information systems designed to help managers to select one of the many alternative solutions to a problem. A DSS is an interactive computer based information system with an organized collection of models, people, procedures, software, databases, telecommunication, and devices, which helps decision makers to solve unstructured or semi-structured business problems. Web mining is the application of data mining techniques to discover patterns from the World Wide Web. Web mining can be divided into three different types – Web usage mining, Web content mining and Web structure mining. Recommender systems (RS) aim to capture the user behavior by suggesting/recommending users with relevant items or services that they find interesting in. Recommender systems have gained prominence in the field of information technology, e-commerce, etc., by inferring personalized recommendations by effectively pruning from a universal set of choices that directed users to identify content of interest.

scholarly journals Hierarchical Context Enabled Recurrent Neural Network for Recommendation

2019 ◽  
Vol 33 ◽  
pp. 4983-4991 ◽  
Author(s):  
Kyungwoo Song ◽  
Mingi Ji ◽  
Sungrae Park ◽  
Il-Chul Moon
Keyword(s):  
Neural Network ◽  
Recommendation System ◽  
Sequential Model ◽  
User Interests ◽  
Gate Structure ◽  
Term Dependency ◽  
User History ◽  
Personal Interests ◽  
Over Time

A long user history inevitably reflects the transitions of personal interests over time. The analyses on the user history require the robust sequential model to anticipate the transitions and the decays of user interests. The user history is often modeled by various RNN structures, but the RNN structures in the recommendation system still suffer from the long-term dependency and the interest drifts. To resolve these challenges, we suggest HCRNN with three hierarchical contexts of the global, the local, and the temporary interests. This structure is designed to withhold the global long-term interest of users, to reflect the local sub-sequence interests, and to attend the temporary interests of each transition. Besides, we propose a hierarchical context-based gate structure to incorporate our interest drift assumption. As we suggest a new RNN structure, we support HCRNN with a complementary bi-channel attention structure to utilize hierarchical context. We experimented the suggested structure on the sequential recommendation tasks with CiteULike, MovieLens, and LastFM, and our model showed the best performances in the sequential recommendations.

Research of Recommended Service of Mobile Terminal Based on User Behavior Analysis

2011 ◽  
Vol 109 ◽  
pp. 577-581
Author(s):  
Wei Liu ◽  
Dong Mei Mu ◽  
Dao Li Huang ◽  
Ji Hao
Keyword(s):  
Behavior Analysis ◽  
Recommendation System ◽  
User Behavior ◽  
Mobile Terminal ◽  
Network Access ◽  
Analysis Model ◽  
User Behavior Analysis ◽  
User Clustering ◽  
Transfer Of Information ◽  
User Recommendation

Due to its portability, mobile terminals (mobile phones and similar devices) have become an transfer of information, between people as well as an important tool for network access. Based on user behavior analysis, using the information of data warehouse will be a reasonable quantification of qualitative indicators, draw the user a variety of potential semantic behavior, and user clustering and dimension reduction, the establishment of a recommendation based on user behavior analysis model . This paper based on user behavior analysis, first extract the user factors into the model are data on these factors reduce the dimensions of the conclusion that the targeted user recommendation system, and such user back into the model test to verify the target User's accuracy.

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