InBox: Recommendation with Knowledge Graph using Interest Box Embedding
Summary: InBox represents KGs as points/boxes and models user interests as boxes containing item points, enabling set-like interest sets and concept composition via box intersections. Outperforms HAKG/KGIN on recommendation benchmarks. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Zezhong Xu (China Mobile; Zhejiang University)
- 2. Yincen Qu (Alibaba)
- 3. Wen Zhang (Ant Financial; Zhejiang University)
- 4. Lei Liang (Ant Financial)
- 5. Huajun Chen (Ant Financial; Zhejiang University)
BibTeX Citation
@article{xu_vldb24,
title = {{InBox: Recommendation with Knowledge Graph using Interest Box Embedding}},
author = {Xu, Zezhong and Qu, Yincen and Zhang, Wen and Liang, Lei and Chen, Huajun},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {13},
pages = {4641--4654},
doi = {10.14778/3704965.3704972},
url = {https://doi.org/10.14778/3704965.3704972},
year = {2024}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 65 | Freebase: A Collaboratively Created Graph Database For Structuring Human Knowledge | 2008 | SIGMOD | 0.00038697603 |
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