Projekter pr. år
Abstract
Knowledge Graphs (KGs) have been integrated in several models of recommendation to augment the informational value of an item by means of its related entities in the graph. Yet, existing datasets only provide explicit ratings on items and no information is provided about users' opinions of other (non-recommendable) entities. To overcome this limitation, we introduce a new dataset, called the MindReader dataset, providing explicit user ratings both for items and for KG entities. In this first version, the MindReader dataset provides more than 102 thousands explicit ratings collected from 1,174 real users on both items and entities from a KG in the movie domain. This dataset has been collected through an online interview application that we also release as open source. As a demonstration of the importance of this new dataset, we present a comparative study of the effect of the inclusion of ratings on non-item KG entities in a variety of state-of-the-art recommendation models. In particular, we show that most models, whether designed specifically for graph data or not, see improvements in recommendation quality when trained on explicit non-item ratings. Moreover, for some models, we show that non-item ratings can effectively replace item ratings without loss of recommendation quality. This finding, in addition to an observed greater familiarity from users towards certain descriptive entities than movies, motivates the use of KG entities for both warm and cold-start recommendations.
Originalsprog | Engelsk |
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Titel | CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management |
Antal sider | 8 |
Forlag | Association for Computing Machinery |
Publikationsdato | 19 okt. 2020 |
Sider | 2975-2982 |
ISBN (Elektronisk) | 9781450368599 |
DOI | |
Status | Udgivet - 19 okt. 2020 |
Begivenhed | 29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - Virtual, Online, Irland Varighed: 19 okt. 2020 → 23 okt. 2020 |
Konference
Konference | 29th ACM International Conference on Information and Knowledge Management, CIKM 2020 |
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Land/Område | Irland |
By | Virtual, Online |
Periode | 19/10/2020 → 23/10/2020 |
Sponsor | ACM SIGIR, ACM SIGWEB |
Fingeraftryk
Dyk ned i forskningsemnerne om 'MindReader: Recommendation over Knowledge Graph Entities with Explicit User Ratings'. Sammen danner de et unikt fingeraftryk.Projekter
- 1 Igangværende
-
Poul Due Jensen Professorate in Big Data and Artificial Intelligence
Hose, K., Jendal, T. E. & Hansen, E. R.
01/11/2019 → 31/10/2024
Projekter: Projekt › Forskning