Name
Abstract | ||
|---|---|---|
With the increasing popularity of micro-video sharing where people shoot short-videos effortlessly and share their daily stories on social media platforms, the micro-video recommendation has attracted extensive research efforts to provide users with micro-videos that interest them. In this paper, a hypothesis we explore is that, not only do users have multi-modal interest, but micro-videos have multi-modal targeted audience segments. As a result, we propose a novel framework User-Video Co-Attention Network (UVCAN), which can learn multi-modal information from both user and microvideo side using attention mechanism. In addition, UVCAN reasons about the attention in a stacked attention network fashion for both user and micro-video. Extensive experiments on two datasets collected from Toffee present superior results of our proposed UVCAN over the state-of-the-art recommendation methods, which demonstrate the effectiveness of the proposed framework.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3308558.3313513 | WWW '19: The Web Conference
San Francisco
CA
USA
May, 2019 |
Keywords | Field | DocType |
Recommendation, attention mechanism, deep learning, micro-video, personalization | World Wide Web,Social media,Computer science,Popularity,Artificial intelligence,Deep learning,Personalization | Conference |
ISBN | Citations | PageRank |
978-1-4503-6674-8 | 4 | 0.39 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shang Liu | 1 | 9 | 1.14 |
| Zhenzhong Chen | 2 | 1244 | 101.41 |
| Hongyi Liu | 3 | 4 | 0.39 |
| Xinghai Hu | 4 | 4 | 0.39 |