Abstract: Techniques are generally described for machine learning exampled-based annotation of image data. In some examples, a first machine learning model may receive a query image comprising a first depiction of an object-of-interest. In some examples, the first machine learning model may receive a target image representing a scene in which a second depiction of the object-of-interest is visually represented. In various examples, the first machine learning model may generate annotated output image data that identifies a location of the second depiction of the object-of-interest within the target image. In some examples, an object detection model may be trained based at least in part on the annotated output image data.

Abstract: Embodiments of a contextualized visual search (CVS) system are disclosed capable of isolating target images of items that contain instances of a previously-unseen query image from a large database of target images. In embodiments, the system is used to implement an interactive query interface of an e-commerce portal, which allows the user to specify the query image (e.g. a logo) to be searched. The system converts the query image into a feature vector using a first machine learning model, and compares the feature vector to feature vectors of target images using a second machine learning model to find matching target images that contain an instance of the query image. The system then returns a query result indicating a list of items associated with matched target images. In embodiments, the query results may be ranked based on a set of personalized factors associated with the user.

Abstract: Devices and techniques are generally described for content classification. In some examples, first item data representing a first item may be received. The first item data may include a plurality of prediction scores output by a machine learning model. Each prediction score of the plurality of prediction scores may be associated with a respective label of a plurality of labels. In some examples, a set of one or more labels among the plurality of labels may be predicted. The set of labels may be predicted as being applicable to the first item for classification of the first item. A determination may be made that the set of one or more labels represents a complete set of labels applicable to the first item. In some examples, the first item may be classified based on the set of one or more labels.

Abstract: A system and method for modeling preferences of customers to drive personalized contextual recommendations using cognitive deep constrained filtering includes receiving a user query on an online retail platform, in response to receiving the user query, performing a first online matrix manipulation and a second online matrix manipulation, and sending a list of ranked recommended products.

Abstract: An approach is provided for evaluating a recommender system. A user's purchase date of an item and attributes of the item are extracted from a test set. Drop probabilities are assigned to the attributes. Using bootstrapping with aggregation, queries for the user and the item are generated by omitting one or more attributes from each of the queries according to the drop probabilities. Data that became available to the recommender system after the purchase date is identified. Without using the identified data and using data that became available to the recommender system before the purchase date, ranked item recommendation sets for the queries are generated. Similarity at rank K (SIM@K) values for the recommendation sets are calculated. Average SIM@K values are calculated over multiple users specified in the test set. Based on the average SIM@K values, the performance of the recommender system is evaluated.

Abstract: A system and method for modeling preferences of customers to drive personalized contextual recommendations using cognitive deep constrained filtering includes receiving a user query on an online retail platform, in response to receiving the user query, performing a first online matrix manipulation and a second online matrix manipulation, and sending a list of ranked recommended products.

Abstract: The present disclosure relates to method and system for automatically identifying one or more issues in one or more tickets of an organization. An issue identification system retrieves a sequence pattern from ticket data received from one or more data sources. The issue identification system generates one or more first sub-sequence patterns of the n-grams from the sequence pattern. Further, frequency of occurrence and Part-of-Speech (POS) weightage of each of the one or more first sub-sequence patterns of the n-grams are determined by the issue identification system. A first score is determined for each of the one or more first sub-sequence patterns of the n-grams based on both the frequency and the POS weightage. Upon determining the first score, the issue identification system identifies one or more issues in the one or more tickets automatically based on the first sub-sequence pattern of the n-grams associated with a highest first score.

Abstract: The present disclosure relates to method and system for automatically identifying one or more issues in one or more tickets of an organization. An issue identification system retrieves a sequence pattern from ticket data received from one or more data sources. The issue identification system generates one or more first sub-sequence patterns of the n-grams from the sequence pattern. Further, frequency of occurrence and Part-of-Speech (POS) weightage of each of the one or more first sub-sequence patterns of the n-grams are determined by the issue identification system. A first score is determined for each of the one or more first sub-sequence patterns of the n-grams based on both the frequency and the POS weightage. Upon determining the first score, the issue identification system identifies one or more issues in the one or more tickets automatically based on the first sub-sequence pattern of the n-grams associated with a highest first score.