ColorfulFeedback: Enhancing Interest Prediction Performance through Multi-dimensional Labeled Feedback from Users
| Autor: | Kota Tsubouchi, Yuki Maeda, Tatsuru Higurashi, Shuji Yamaguchi |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry media_common.quotation_subject User modeling 05 social sciences 050301 education Inference 050801 communication & media studies Recommender system Hybrid approach Machine learning computer.software_genre Recommendation model 0508 media and communications Multi dimensional Quality (business) Artificial intelligence business 0503 education computer media_common |
| Zdroj: | WSDM |
| DOI: | 10.1145/3437963.3441700 |
| Popis: | Recommendation systems help to predict user demand and improve the quality of services offered. While the performance of a recommendation system depends on the quality and quantity of feedback from users, the two major approaches to feedback sacrifice quality for quantity or vice versa; implicit feedback is more abundant but less reliable, while explicit feedback is more credible but harder to collect. Although a hybrid approach has the potential to combine the strengths of both kinds of feedback, the existing approaches using explicit feedback are not suitable for such a combination. In this study, we design a novel feedback suitable for the hybrid approach and use it improve the performance of a recommendation system. The system enables us to collect more varied and less biased feedback from users. It improves performance without requiring major changes to the inference model. It also provides a unique and rich source of information of the model itself. We demonstrate an application of Colorful Feedback showing how it can improve an existing recommendation model. |
| Databáze: | OpenAIRE |
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