Abstract: Adapting user interface designs for specific tasks performed by different users is a challenging yet important problem. Automatically adapting visualization designs to users and contexts (e.g., tasks, display devices, environments, etc.) can theoretically improve human-computer interaction to acquire insights from complex datasets. However, effectiveness of any specific visualization is moderated by individual differences in knowledge, skills, and abilities for different contexts. A modeling framework called Personalized Recommender System for Information visualization Methods via Extended matrix completion (PRIME) is described for recommending the optimal visualization designs for individual users in different contexts.

Abstract: Computation pipeline-dataset exploration, visualization, and recommendation concepts are described. For example, a method can include learning first visualization latent-space features of different datasets represented in a first two-dimensional latent space and second visualization latent-space features of different computation pipelines represented in a second two-dimensional latent space. The method can also include modeling dataset-pipeline interactions between the different datasets and the different computation pipelines based on the first visualization latent-space features and the second visualization latent-space features. The method can also include learning relationships between the first visualization latent-space features and the second visualization latent-space features based on modeling the dataset-pipeline interactions. In another example, the method can further include generating a visual representation of the relationships and the dataset-pipeline interactions.

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.