Patent application number | Description | Published |
20120158620 | HUMAN-ASSISTED TRAINING OF AUTOMATED CLASSIFIERS - Many computing scenarios involve the classification of content items within one or more categories. The content item set may be too large for humans to classify, but an automated classifier (e.g., an artificial neural network) may not be able to classify all content items with acceptable accuracy. Instead, the automated classifier may calculate a classification confidence while classifying respective content items. Content items having a low classification confidence may be sent to a human classifier, and may be added, along with the categories identified by the human classifier, to a training set. The automated classifier may then be retrained using the training set, thereby incrementally improving the classification confidence of the automated classifier while conserving the involvement of human classifiers. Additionally, human classifiers may be rewarded for classifying the content items, and the costs of such rewards may be considered while selecting content items for the training set. | 06-21-2012 |
20130211950 | RECOMMENDER SYSTEM - Embodiments of the invention provide methods and apparatus for recommending items from a catalog of items to a user by parsing the catalog of items into a plurality of catalog clusters of related items and recommending catalog items to the user from catalog clusters to which items previously preferred by the user belong. | 08-15-2013 |
20130218907 | RECOMMENDER SYSTEM - Embodiments of the invention provide methods and apparatus for recommending items from a catalog of items to users in a population of users by generating trait vectors that represent items in the catalog responsive to explicit and/or implicit preference data for a group of less than all the users and using the trait vectors to recommend items to users in the population that are not in the group. | 08-22-2013 |
20140129500 | Efficient Modeling System - A technique for efficiently factoring a matrix in a recommendation system. Usage data for a large set of users relative to a set of items is provided in a usage matrix R. To reduce computational requirements, the usage matrix is sampled to provide a reduced matrix R′. R′ is factored into a user matrix U′ and an item matrix V. User vectors in U′ and V are initialized and then iteratively updated to arrive at an optimal solution. The reduced matrix can be factored using the computational resources of a single computing device, for instance. Subsequently, the full user matrix U is obtained by fixing V and analytically minimizing an error in UV=R+error. The computations of this analytic solution can be divided among a set of computing devices, such as by using a map and reduce technique. Each computing device solves the equation for different respective subset of users. | 05-08-2014 |
20140181121 | FEATURE EMBEDDING IN MATRIX FACTORIZATION - In various embodiments, systems and methods are provided for enhancing media content recommendations by using feature vectors. An enhanced-matrix having a first portion and a second portion is received. The first portion of the enhanced-matrix includes a user-item matrix and the second portion of the enhanced-matrix includes a feature-item matrix. Each entry in the feature-item matrix is item metadata. An item-stem vector is determined based on a weighted sum of each of the feature vectors associated with the item. An item-latent-trait vector is generated based on the item-stem vector and an item-offset vector. The item-offset vector is an item vector for the item in the user-item matrix. One or more recommended-media content derived based on the item-latent-trait vector is provided. | 06-26-2014 |
20150073932 | Strength Based Modeling For Recommendation System - Example apparatus and methods provide a recommendation to a user about a product they may wish to consider purchasing. One method produces a single indication concerning a relationship between a user and an item with which the user has interacted. The single indication identifies whether the user likes the item and the degree to which the user likes the item. The single indication is independent of user signals processed to compute the single indication. The single indication is produced by a signal deriver that is loosely coupled to a model of users and items. The model may be a matrix upon which matrix factorization can be performed. Although matrix factorization is performed, it is performed on vectors whose elements are independent of the signals processed by the signal deriver. Since users may have different preferences at different times, the degree to which the user likes the item may be manipulated. | 03-12-2015 |
20150112801 | MULTIPLE PERSONA BASED MODELING - Matrix factorization techniques may be employed to identify different tastes based on user history information for a user profile and to provide item recommendations for the various tastes. An item model may be generated that includes item vectors, each item vector representing an item from a catalog of items. An item vector from the item model may be identified for each of a number of items identified in information for a user profile. The item vectors may be grouped into different clusters, and a taste vector may be generated for each cluster based on item vectors in each cluster. Each taste vector may be used to select item recommendations that may be combined in a set of recommendations provided for presentation to one or more users associated with the user profile. | 04-23-2015 |
20150193548 | Recommendation System With Metric Transformation - Example apparatus and methods transform a non-metric latent space produced by a matrix factorization process to a higher dimension metric space by applying an order preserving transformation to the latent space. The transformation preserves the order of the results of an inner product operation defined for the latent space. The higher dimension metric space may be queried for the results to different requests. Example apparatus and methods may assign every user i a vector u | 07-09-2015 |