Name
Abstract | ||
|---|---|---|
With the rapid development of information technology, Internet of Things and other technologies, various applications in the Internet space are emerging in an endless stream, triggering an explosive increase in data scale. Intelligent recommendation systems can efficiently filter out massive data sentiment analysis is one of the key technologies of Natural Language Processing (NLP) and intelligent recommendation systems. Finding out the reasons behind certain emotional expressions in texts through information technology has received widespread attention. Based on this, this paper proposes a text-aware recommendation model based on multi-attention neural network model to solve this problem. First, we use a modified LDA and paragraph vector learning framework to obtain the text vector representation, then capture the context information of the text through the Bi-LSTM layer, and finally input the representation into the CNN layer to classify and predict emotional factors. Experimental results show that our proposed method is significantly better than the most advanced baseline method. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-82136-4_48 | KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, PT I |
Keywords | DocType | Volume |
Context, Text-aware, Multi-attention mechanism, Deep learning, Recommender system, Neural networks | Conference | 12815 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.35 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gang Qiu | 1 | 1 | 1.36 |
| Xiaoli Yu | 2 | 1 | 0.35 |
| Liping Jiang | 3 | 1 | 0.35 |
| Baoying Ma | 4 | 1 | 0.35 |