Name
Title | ||
|---|---|---|
"It doesn't look good for a date" - Transforming Critiques into Preferences for Conversational Recommendation Systems. |
Abstract | ||
|---|---|---|
Conversations aimed at determining good recommendations are iterative in nature. People often express their preferences in terms of a critique of the current recommendation (e.g., "It doesn't look good for a date"), requiring some degree of common sense for a preference to be inferred. In this work, we present a method for transforming a user critique into a positive preference (e.g., "I prefer more romantic") in order to retrieve reviews pertaining to potentially better recommendations (e.g., "Perfect for a romantic dinner"). We leverage a large neural language model (LM) in a few-shot setting to perform critique-to-preference transformation, and we test two methods for retrieving recommendations: one that matches embeddings, and another that fine-tunes an LM for the task. We instantiate this approach in the restaurant domain and evaluate it using a new dataset of restaurant critiques. In an ablation study, we show that utilizing critique-to-preference transformation improves recommendations, and that there are at least three general cases that explain this improved performance. |
Year | Venue | DocType |
|---|---|---|
2021 | EMNLP | Conference |
Volume | Citations | PageRank |
2021.emnlp-main | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Victor S. Bursztyn | 1 | 0 | 0.68 |
| Jennifer Healey | 2 | 1643 | 285.32 |
| Nedim Lipka | 3 | 0 | 1.69 |
| Eunyee Koh | 4 | 0 | 0.34 |
| Doug Downey | 5 | 0 | 1.35 |
| Larry Birnbaum | 6 | 0 | 0.34 |