Patent application number | Description | Published |
20090282029 | METHOD, A SYSTEM AND A COMPUTER PROGRAM PRODUCT FOR DETECTING A LOCAL PHENOMENON - A system for detecting a local phenomenon, the system includes an interface for receiving queries information from a system for retrieving art related media, and a processor, configured to: (a) create a first local popularity chart, wherein the creating of the first local popularity chart includes enumerating, for each geographic area of a group of sampled geographic areas, identical query strings of queries that are included in a group of queries; (b) create a first global popularity chart, wherein the creating of the first global popularity chart includes enumerating identical query strings of the queries that are included in the group of queries; and (c) select at least one query string in response to a scoring of the query string at the first local popularity chart and to a scoring of the query string at the first global popularity chart; wherein the group of queries includes queries which were queried during a first period of time. | 11-12-2009 |
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 |
20140372430 | AUTOMATIC AUDIENCE DETECTION FOR MODIFYING USER PROFILES AND MAKING GROUP RECOMMENDATIONS - Disclosed herein is a system and method for determining that a current user profile in a system should be modified or changed. An audience detection component detects that a characteristic has been detected that does not match at least one characteristic in the current user profile. The audience detection component determines how the profile should be modified or restricted based on the inputs received from the sensors. The modified profile is then provided to a recommender system so that appropriate content may be suggested to the consumers without any further intervention or action required by the user. | 12-18-2014 |
20150073931 | FEATURE SELECTION FOR RECOMMENDER SYSTEMS - Disclosed herein is a system and method for identifying features of items that are more relevant for making recommendations to consumers for content that they may be interested in. The system determines the similarity between items that are recommend and items in the user's history and compares that similarity measure to the similarity measure calculated for a random item on the same features. From this similarity measure the relative impactfullness of a particular feature on a recommendation can be determined. | 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 |