Optimizing Item and Subgroup Configurations for Social-Aware VR Shopping
Summary: Proposes SVGIC, a problem of configuring displayed items and subgroup partitions for social-aware VR group shopping. APX-hard and NP-hard to approximate within 32-ε; a 4-approx CSF-based algorithm; experiments on real VR data and a user study with HTC VIVE show ~30% improvement over baselines. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Shao-Heng Ko
- 2. Hsu-Chao Lai
- 3. Hong-Han Shuai
- 4. Wang-Chien Lee
- 5. Philip S. Yu
- 6. De-Nian Yang
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,603 | Maximum Co-located Community Search in Large Scale Social Networks | 2018 | VLDB | 8.4666465e-05 |
| 5,730 | Walk, Not Wait: Faster Sampling Over Online Social Networks | 2015 | VLDB | 5.3506029e-05 |
| 6,427 | From Group Recommendations to Group Formation | 2015 | SIGMOD | 5.0670573e-05 |
| 7,503 | Heterogeneous Recommendations: What You Might Like To Read After Watching Interstellar | 2017 | VLDB | 4.7180617e-05 |
| 12,011 | Willingness Optimization for Social Group Activity | 2014 | VLDB | 4.1945683e-05 |
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