Papers with TripAdvisor
Explainable Recommendation with Personalized Review Retrieval and Aspect Learning (2023.acl-long)
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| Challenge: | Recent years have witnessed a growing interest in the development of explainable recommendation models. |
| Approach: | They propose a model that combines prediction and generation tasks to produce more persuasive explanations by obtaining additional information from the training sets. |
| Outcome: | The proposed model outperforms state-of-the-art models on three datasets and shows that it is more persuasive than previous models. |
A Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment Classification (N19-1)
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| Challenge: | Existing weakly supervised methods for document-level multi-aspect sentiment classification are not easy to obtain. |
| Approach: | They propose a variational approach to weakly supervised document-level multi-aspect sentiment classification using target-opinion word pairs as "supervision" they aim to learn a sentiment polarity classifier by optimizing the lower bound . |
| Outcome: | The proposed method outperforms weakly supervised baselines on TripAdvisor and BeerAdvocate datasets and can be comparable to state-of-the-art supervised methods with hundreds of labels per aspect. |
Diversified Multiple Instance Learning for Document-Level Multi-Aspect Sentiment Classification (2020.emnlp-main)
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| Challenge: | Experimental results show that D-MILN outperforms recent weakly-supervised baselines . document-level multi-aspect sentiment classification requires a lot of manual aspect-level annotations - which is time-consuming and laborious . |
| Approach: | They propose a novel Diversified Multiple Instance Learning Network to achieve DMSC with only document-level weak supervision. |
| Outcome: | The proposed method outperforms weakly-supervised baselines on TripAdvisor and BeerAdvocate datasets. |
HotelRec: a Novel Very Large-Scale Hotel Recommendation Dataset (2020.lrec-1)
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| Challenge: | State-of-the-art deep learning-based recommender systems require large datasets to achieve their best performance. |
| Approach: | They propose to use TripAdvisor to build a large-scale hotel recommendation dataset with 50 million reviews. |
| Outcome: | The proposed dataset is the largest publicly available hotel recommendation dataset, based on TripAdvisor, with 50 million reviews. |