Toward Social Media Content Recommendation Integrated with Data Science and Machine Learning Approach for E-Learners
Abstract
:1. Introduction
- A real-time system which provides top-ranked Twitter user networks to e-learners from Twitter, according to their context, history and profiles.
- The proposed system recommends top-ranked articles according to the e-learner’s context, e-learner’s history, and e-learner’s profiles from DBLP.
- The system also makes recommendations to e-learners from a local database.
- The main objective of this study is the use of data mining and machine learning approaches for social media content recommendation.
2. Literature Review
2.1. Recommendation System
2.2. Twitter Recommendation
2.3. DBLP Recommendation
2.4. Reinforcement Learning Recommendation
3. Social Media Content Recommendation for E-Learners
3.1. E-Learners Recommendation System
3.2. Dataset
- Collecting data;
- Cleaning data;
- Manipulate missing values;
- Missing value extraction;
- Discovering the available features.
3.3. Data Mining and Visualization
- Twitter and DBLP article recommendation for e-learners based on tweet frequency, selected articles and user preference;
- Time series analysis based on Monthly and daily analysis;
- Twitter and DBLP platform analysis based on e-learner preferences;
- Twitter and DBLP platform analysis based on e-learner clicked links;
3.3.1. Time Series Analysis
3.3.2. Twitter API Analysis Based on Profile Address
3.4. Discover Patterns and Features
3.5. Interaction Model for the Proposed Recommendation Platform
4. Predictive Analysis of Twitter and DBLP Data Using Reinforcement Learning
Reinforcement Learning Optimization
5. Prediction Result of Twitter and DBLP Platform
5.1. Experimental Environment and Setup
5.2. Performance Evaluation
- Mean Square Error This statistical evaluation measure the relationship between predicted value and actual value based on the mentioned Equation (9).
- Mean Absolute ErrorThis statistical evaluation measures the square of differences between predicted value and actual value based on the mentioned Equation (10).
- Root Mean Square ErrorThis statistical evaluation measure the error rate, error size based on the target value which mentioned in Equation (11).
5.3. Prediction Results
5.4. Recommendation Results
Comparison and Baseline
6. Conclusions and Future Work
Author Contributions
Funding
Conflicts of Interest
References
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Authors | Objective | Advantage |
---|---|---|
Long Zhao et al., Zemin Liu et al. (2020) [55] | Solving the large action space issue based on the Reinforcement Learning Algorithm. | Reinforcement learning solves the large action space issue. |
Bushra Alhijawi et al. [56], Yousef Kilani et al. (2020) | Recommendation system classification on MRS, TPCRS, SRS and CRS | Classifying recommendation system to avoid the overloading issue. |
Ruotsalo et al. [57] (2013) | Raising the digital cultural heritage accessibility | It is useful to apply to any data. |
Braunhofer et al. [58] (2014) | Place of interest (POI) recommendation | Generate related recommendations with higher useability. |
Elahi et al. (2013) [59] | POI-based user personality recommendation | Desist the cold start issue. |
Ostuni et al. [60] (2013) | Movie theatre recommendation | Clear the content-based recommendation results. |
Braunhofer et al. [61] (2014) | User personality recommendation based on contact preferences | Presenting more related recommendations based on the higher rating. |
Noguera et al. (2012) [62] | Users’ physical locations recommendation | Useful in e-tourism. The ability to have a 3D map. |
Bouneffouf et al. [63] (2012) | Dynamic exploration recommendation | Optimal value selection while avoiding the traditional algorithms. |
Ge et al. (2010) [64] | Parking position recommendation | Increasing business success probability. Providing various optimal driving routes based on online processing time. |
Statistics | Numerical Values |
---|---|
Number of directions | 744.456 |
Number of articles | 567.916 |
Number of Users | 582.933 |
Avg/Max/Min of time | 3.5/6.4/1.6 |
Training Data | 70% |
Test Data | 30% |
# | Features | Description |
---|---|---|
1 | Tweet | The information which users share together |
2 | Projects | The information of various projects (question and answer) |
3 | Comments | Comments for shared topic |
4 | Photos | Shared photos by various users |
5 | News | Shared daily news |
6 | Conferences | Upcoming conferences or opinions about previous conferences |
7 | Articles | Published articles |
8 | Publication Access Point | Reference pages or article access information |
9 | Publication Time | Article publication time |
10 | Publication Date | Article publication date |
# | Features | Description |
---|---|---|
1 | Time series | Applying time series analysis in this system causes us to extract the information related to visited links per day or download and sharing information per day and, similarly, total average per month |
2 | E-learner profile details | Based on the e-learners profile, the major interest of the user on various topics and user clicks and shared tweets, news and articles are extracted. |
3 | statistical features | Extract the histogram, error rate, etc. from raw dataset for articles frequency. |
4 | Article types | Generate various article topics, titles, etc. |
5 | Tweet types | Generate different tweet information, comments, news and shared links. |
Component | Description |
---|---|
Programming language | WinPython–3.6.2, IDE Jupyter Notebook |
Operating system | Windows 10 64bit |
Browser | Google Chrome, opera |
GPU | Nvidia GForce 1080 |
Library and framework | Web Service |
CPU | Intel(R) Core(TM) i7-8700 CPU @3.20 GHz |
Memory | 32 GB |
Recommendation Modules | Reinforcement Learning |
Optimization Algorithm | Model Free optimization |
Number | Loading T. (sec) | Searching T. (sec) | Execution T. (sec) |
---|---|---|---|
1 | 2.0883 | 0.0350 | 2.4505 |
2 | 0.5101 | 0.0348 | 0.5601 |
3 | 0.0012 | 0.0377 | 0.0401 |
4 | 1.7510 | 0.0627 | 1.8237 |
5 | 2.0883 | 0.0344 | 2.1331 |
6 | 2.0883 | 0.0344 | 2.4433 |
7 | 1.7510 | 0.0616 | 1.8226 |
8 | 1.7510 | 0.0358 | 1.8068 |
9 | 2.0883 | 0.0013 | 2.1017 |
10 | 1.7510 | 0.0400 | 1.8058 |
Technique | Recommendation Diversity |
---|---|
LR | 0.2944 |
FM | 0.3125 |
W&D | 0.1758 |
LinUCB | 0.3747 |
HLinUCB | 0.2434 |
DN | 0.2657 |
DDQN | 0.2146 |
DDQN + U | 0.2824 |
DDQN + U + EG | 0.2118 |
DDQN + U + DBGD | 0.2327 |
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Share and Cite
Shahbazi, Z.; Byun, Y.C. Toward Social Media Content Recommendation Integrated with Data Science and Machine Learning Approach for E-Learners. Symmetry 2020, 12, 1798. https://doi.org/10.3390/sym12111798
Shahbazi Z, Byun YC. Toward Social Media Content Recommendation Integrated with Data Science and Machine Learning Approach for E-Learners. Symmetry. 2020; 12(11):1798. https://doi.org/10.3390/sym12111798
Chicago/Turabian StyleShahbazi, Zeinab, and Yung Cheol Byun. 2020. "Toward Social Media Content Recommendation Integrated with Data Science and Machine Learning Approach for E-Learners" Symmetry 12, no. 11: 1798. https://doi.org/10.3390/sym12111798
APA StyleShahbazi, Z., & Byun, Y. C. (2020). Toward Social Media Content Recommendation Integrated with Data Science and Machine Learning Approach for E-Learners. Symmetry, 12(11), 1798. https://doi.org/10.3390/sym12111798