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 \u2018parent\u2019 tags and the tags themselves can be associated with several \u2018parent\u2019 tags in a hierarchical manner. Our model learns item representations that are guided by the \u2018parent\u2019 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.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 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