Abstract: The present technology is directed to high performing trained machine learning models for information retrieval in a web store. In some embodiments, for example, when a search query is received from a user of an online retailer, a computer system associated with the online retailer accesses measurements of performance of sets of search results returned in response to previous user search queries. Each of the previous search results set is a set that was ranked by a machine learning model selected from a store of machine learning models that are each trained to rank search results. Based on the measurements of performance, the computer system selects a machine learning model to rank search results for a response to the received search query. The ranked search results are provided for output to the user.

Abstract: An image recommendation system extracts multiple sets of feature vectors from each of a plurality of images in an image catalog using multiple image classification algorithms. For a first image in the plurality of images, the recommendation system generates multiple similarity scores between the first image and each of one or more other images in the image catalog based on the feature vectors extracted from the first image and the one or more other images using each of the multiple image classification algorithms. A first set of weights is applied to the multiple similarity scores to generate respective weighted similarity scores between the first image and each of the one or more other images. The weighted similarity scores are stored, and used to select images that are similar to the first image.

Abstract: The present technology is directed to high performing trained machine learning models for information retrieval in a web store. In some embodiments, for example, when a search query is received from a user of an online retailer, a computer system associated with the online retailer accesses measurements of performance of sets of search results returned in response to previous user search queries. Each of the previous search results set is a set that was ranked by a machine learning model selected from a store of machine learning models that are each trained to rank search results. Based on the measurements of performance, the computer system selects a machine learning model to rank search results for a response to the received search query. The ranked search results are provided for output to the user.

Abstract: A recommendation system and method access a recommendation bundle pool including multiple recommendation algorithms, each of which is capable of generating one or more recommendations. A first recommendation bundle comprising two or more recommendation algorithms is selected from the pool. Using the first recommendation bundle, recommendations are generated to provide to visitors to a website. When the recommendation system detects a triggering condition for a scaling cycle, the recommendation system applies a scaling mechanism to increase an exploration of additional recommendation bundles from the recommendation bundle pool. Based on the exploration, the recommendation system selects a second recommendation bundle including algorithms selected from the pool. Recommendations are generated using the second recommendation bundle.