Abstract: Retrieving content items in response to a query in a way that increases user satisfaction and increases chances of users consuming a retrieved content item is not trivial. One retrieval strategy may include dividing the content items into buckets according to a dimension about the content items and retrieving a top K number of items from different buckets to balance semantic affinity and the dimension. Choosing an optimal K for different buckets for a given query can be a challenge. Reinforcement learning can be used to train and implement an agent model that can choose the optimal K for different buckets.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for model customization for domain-specific tasks. An embodiment may select a pre-trained embedding model trained with a first dataset. The embodiment may determine a second dataset for a target domain. Based on target embeddings for data indicative of the target domain. The embodiment may transform the second dataset from a first format to a second format associated with the target domain. The embodiment may modify the weights of the pre-trained embedding model based on the transformed second dataset. Based on the modified weights, the embodiment may transform the pre-trained embedding model into a target embedding model for the target domain. The embodiment may then generate an efficacy score for the target embedding model based on a task of the target domain performed by the target embedding model.

Abstract: A comparison between items for sale in a marketplace and items for sale at an external retailer is used to generate one or more recommended actions for the marketplace. For example, if the marketplace has a comparable quantity of available items in a product cluster with better prices, a recommendation to promote the items based on price may be generated As another example, if the marketplace has a better variety of items available in the product cluster, a recommendation to promote the items that are unavailable at the external retailer may be generated. As a third example, if the marketplace has fewer items available in the product cluster, a recommendation to procure additional inventory may be generated. As yet another example, if the marketplace has items available in the product cluster at a higher price than the external retailer, a recommendation to reduce prices may be generated.