Name
Abstract | ||
|---|---|---|
The rapid development of artificial intelligence (AI) has motivated extensive research on dialog system. Using dialog system to automatize customer service is a common practice in many business fields. In this paper, we investigate a novel task to recommend response for customer service agents of each shop. A major challenge is the problem of data insufficiency for each shop. Meanwhile, we want to keep the personalized information for shops with very different commodities. To deal with such problems, we propose a LSTM (Long Short-Term Memory) Neuron Tensor Network architecture to encode the common features of all shops' data and model the personalized features of each shop. Extensive experiments demonstrate that our method outperforms four baseline methods evaluated by recall metric. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-59072-1_5 | ADVANCES IN NEURAL NETWORKS, PT I |
Keywords | Field | DocType |
Response recommendation,Dialogue system,Neural network | Computer science,Network architecture,Artificial intelligence,Service level requirement,Dialog system,Artificial neural network,Customer Service Assurance,ENCODE,World Wide Web,Customer advocacy,Recall,Multimedia,Machine learning | Conference |
Volume | ISSN | Citations |
10261 | 0302-9743 | 1 |
PageRank | References | Authors |
0.36 | 8 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cuihua Ma | 1 | 1 | 0.70 |
| Ping Guo | 2 | 601 | 85.05 |
| Xin Xin | 3 | 58 | 7.73 |
| Xiaoyu Ma | 4 | 1 | 0.70 |
| Yanjie Liang | 5 | 8 | 5.83 |
| Shaomin Xing | 6 | 1 | 0.36 |
| Li Li | 7 | 1 | 0.70 |
| Shaozhuang Liu | 8 | 1 | 0.70 |