Improving Recommendation Accuracy Using Social Network of Owners in Social Internet of Vehicles
Abstract
:1. Introduction
2. Related Work
- success rate of recommendations achieved from the network; and
- comparability of PoIs utilization, with focus on resistance to highly skewed distribution.
“Given that a long-term and consistent connectivity transforms into interactions (and more trustworthy information sharing), the prospect of the spread of useful information in the network increases with (favorable) evolution of the network, occurring solely due to internal dynamics of a regular network (such as triadic closure).”
3. Combinatorial Framework
4. The Proposed Model
4.1. Activity Life Cycle
4.2. Quality of Service Variability
4.3. Activity Replacement Strategies
- ReplaceS: Replaces an activity with the help of a strong tie from the SIoV.
- ReplaceW: Replaces an activity with the help of a weak tie from SIoV.
- ReplaceM: Replaces an activity with the help of vehicles’ data of the person on SN with highest degree.
- ReplaceL: Replaces an activity with the help of vehicles’ data of a random person on SN.
4.4. Social Network Model
5. Evaluation and Discussion
5.1. Simulation Environment
5.2. Input Parameters
- Replacement strategy: which has four possible options: ReplaceS, ReplaceW, ReplaceM, and ReplaceL.
- Use of quality enforcement mechanism: the model requires that the quality enforcement mechanism is invoked or not. It is applied exclusively to each strategy.
- Quality update threshold = 50;
- force quality interval = 10;
- persons (vehicles) count = 500; and
- POI % = 3% of 500.
5.3. Statistical Measures
- Number of Good Visits: This parameter counts the total number of Good Visits throughout the simulation. A visit is a Good Visit when, at the time of the visit, the q value of the POI is greater than or equal to the expectation of the visitor.
- Number of Bad Visits: This parameter maintains the total number of Bad Visits throughout the simulation. A visit is a Bad Visit when, at the time of the visit, the q value of the POI is less than the expectation of the visitor.
- Number of No Visits: This parameter counts the number of visits, when the visits are not performed.
5.4. Simulation Results and Discussion
6. Conclusions and Future Work
Future Work
Author Contributions
Funding
Conflicts of Interest
Abbreviations
IoV | Internet of Vehicles |
IoT | Internet of Things |
SIoV | Social IoV |
VIoT | Vehicular Internet of Things |
VANETs | Vehicular Adhoc NETworks |
VSN | Vehicular Social Networks |
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Phase | Strategy | Hypothesis |
---|---|---|
1: without quality enforcement | ReplaceS | Both 1 & 3 |
ReplaceW | ||
ReplaceM | Both 2 & 3 | |
ReplaceL | ||
2: with quality enforcement | ReplaceS | Only 3 |
ReplaceW | ||
ReplaceM | ||
ReplaceL |
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Zia, K.; Shafi, M.; Farooq, U. Improving Recommendation Accuracy Using Social Network of Owners in Social Internet of Vehicles. Future Internet 2020, 12, 69. https://doi.org/10.3390/fi12040069
Zia K, Shafi M, Farooq U. Improving Recommendation Accuracy Using Social Network of Owners in Social Internet of Vehicles. Future Internet. 2020; 12(4):69. https://doi.org/10.3390/fi12040069
Chicago/Turabian StyleZia, Kashif, Muhammad Shafi, and Umar Farooq. 2020. "Improving Recommendation Accuracy Using Social Network of Owners in Social Internet of Vehicles" Future Internet 12, no. 4: 69. https://doi.org/10.3390/fi12040069
APA StyleZia, K., Shafi, M., & Farooq, U. (2020). Improving Recommendation Accuracy Using Social Network of Owners in Social Internet of Vehicles. Future Internet, 12(4), 69. https://doi.org/10.3390/fi12040069