Learning item representations is a key building block in recommender systems research. However, representations often suffer from the cold start problem - a well-known problem in which rare items in the tail of the distribution face insufficient data yielding inadequate representations. In this work, we present a novel hybrid recommender that supports the utilization of hierarchical content-based information to mitigate the cold start problem. In particular, we assume a taxonomy of item tags in which every item is associated with several 'parent' tags and the tags themselves can be associated with several 'parent' tags in a hierarchical manner. Our model learns item representations that are guided by the 'parent' tags of each item which allows propagating relevant information between items sharing the same hierarchy. In addition, the tags are modeled using tag representations that allow propagating information between any two tags that share a common ancestor. Due to space limitation, we focus this work on a recommendations task, however the same approach can be utilized for general representation learning e.g. language models.
|Title of host publication
|Proceedings - 20th IEEE International Conference on Data Mining, ICDM 2020
|Claudia Plant, Haixun Wang, Alfredo Cuzzocrea, Carlo Zaniolo, Xindong Wu
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - Nov 2020
|20th IEEE International Conference on Data Mining, ICDM 2020 - Virtual, Sorrento, Italy
Duration: 17 Nov 2020 → 20 Nov 2020
|Proceedings - IEEE International Conference on Data Mining, ICDM
|20th IEEE International Conference on Data Mining, ICDM 2020
|17/11/20 → 20/11/20
Bibliographical notePublisher Copyright:
© 2020 IEEE.
- Cold start
- Hierarchical models
- Recommender systems
- Representation learning