Name
Abstract | ||
|---|---|---|
Recommendation systems typically rely on the interactions between a crowd of ordinary users and items, ignoring the fact that many real-world communities are notably influenced by a small group of key opinion leaders, whose feedback on items wields outsize influence. With important positions in the community (e.g. have a large number of followers), their elite opinions are able to diffuse to the community and further impact what items we buy, what media we consume, and how we interact with online platforms. Hence, this paper investigates how to develop a novel recommendation system by explicitly capturing the influence from key opinion leaders to the whole community. Centering around opinion elicitation and diffusion, we propose an end-to-end Graph-based neural model - GoRec. Specifically, to preserve the multi-relations between key opinion leaders and items, GoRec elicits the opinions from key opinion leaders with a translation-based embedding method. Moreover, GoRec adopts the idea of Graph Neural Networks to model the elite opinion diffusion process for improved recommendation. Through experiments on Goodreads and Epinions, the proposed model outperforms state-of-the-art approaches by 10.75% and 9.28% on average in Top-K item recommendation.
|
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3336191.3371826 | WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining
Houston
TX
USA
February, 2020 |
Keywords | DocType | ISBN |
Recommendation, Key Opinion Leaders, Graph Neural Networks | Conference | 978-1-4503-6822-3 |
Citations | PageRank | References |
2 | 0.36 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianling Wang | 1 | 43 | 7.58 |
| Kaize Ding | 2 | 51 | 9.72 |
| Ziwei Zhu | 3 | 25 | 7.81 |
| Yin Zhang | 4 | 3492 | 281.04 |
| James Caverlee | 5 | 2484 | 145.47 |