Name
Abstract | ||
|---|---|---|
We propose a new sentiment information-based attention mechanism that helps to identify user reviews that are more likely to enhance the accuracy of a rating prediction model. We hypothesis that highly polarised reviews (strongly positive or negative) are better indicators of the users' preferences and that this sentiment polarity information helps to identify the usefulness of reviews. Hence, we introduce a novel neural network rating prediction model, called SentiAttn, which includes both the proposed sentiment attention mechanism as well as a global attention mechanism that captures the importance of different parts of the reviews. We show how the concatenation of the positive and negative users' and items' reviews as input to SentiAttn, results in different architectures with various channels. We investigate if we can improve the performance of SentiAttn by fine-tuning different channel setups. We examine the performance of SentiAttn on two well-known datasets from Yelp and Amazon. Our results show that SentiAttn significantly outperforms a classical approach and four state-of-the-art rating prediction models. Moreover, we show the advantages of using the sentiment attention mechanism in the rating prediction task and its effectiveness in addressing the cold-start problem. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-030-99736-6_33 | ADVANCES IN INFORMATION RETRIEVAL, PT I |
DocType | Volume | ISSN |
Conference | 13185 | 0302-9743 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xi Wang | 1 | 1 | 1.02 |
| Iadh Ounis | 2 | 3438 | 234.59 |
| Craig Macdonald | 3 | 2588 | 178.50 |