Patent application number | Description | Published |
20090083126 | Methods and Apparatus for Modeling Relationships at Multiple Scales in Ratings Estimation - Systems and techniques for generating item ratings for a user in order to allow for recommendations of selected items for that user. A set of known ratings of different items for a plurality of users is collected and maintained, and these known ratings are used to estimate rating factors influencing ratings, including user and item factors. Initial user and item factors are estimated and new user and item factors are successively added, with the original rating factors being progressively shrunk so as to reduce their magnitude and their contribution to the rating estimation as successive factors are added. When an appropriate number of user and item factors has been estimated, the rating factors are used to estimate ratings of items for a user, and the estimated ratings are employed to generate recommendations for that user. | 03-26-2009 |
20090083258 | Methods and Apparatus for Improved Neighborhood Based Analysis in Ratings Estimation - Systems and techniques for estimation of item ratings for a user. A set of item ratings by multiple users is maintained, and similarity measures for all items are precomputed, as well as values used to generate interpolation weights for ratings neighboring a rating of interest to be estimated. A predetermined number of neighbors are selected for an item whose rating is to be estimated, the neighbors being those with the highest similarity measures. Global effects are removed, and interpolation weights for the neighbors are computed simultaneously. The interpolation weights are used to estimate a rating for the item based on the neighboring ratings, Suitably, ratings are estimated for all items in a predetermined dataset that have not yet been rated by the user, and recommendations are made of the user by selecting a predetermined number of items in the dataset having the highest estimated ratings. | 03-26-2009 |
20090204554 | Direction-aware proximity for graph mining - A method and system for graph mining direction-aware proximity measurements. A directed graph includes nodes and directed edges connecting the nodes. A direction-aware proximity measurement is calculated from a first node to a second node or from a first group of nodes to a second group of nodes. The direction-aware proximity measurement from a first node to second node is based on an escape probability from the first node to the second node. Disclosed herein are methods for efficiently calculating one or multiple direction-aware proximity measurements. The direction-aware proximity measurements can be used in performing various graph mining applications. | 08-13-2009 |
20100030764 | Recommender System Utilizing Collaborative Filtering Combining Explicit and Implicit Feedback with both Neighborhood and Latent Factor Models - Example collaborative filtering techniques provide improved recommendation prediction accuracy by capitalizing on the advantages of both neighborhood and latent factor approaches. One example collaborative filtering technique is based on an optimization framework that allows smooth integration of a neighborhood model with latent factor models, and which provides for the inclusion of implicit user feedback. A disclosed example Singular Value Decomposition (SVD)-based latent factor model facilitates the explanation or disclosure of the reasoning behind recommendations. Another example collaborative filtering model integrates neighborhood modeling and SVD-based latent factor modeling into a single modeling framework. These collaborative filtering techniques can be advantageously deployed in, for example, a multimedia content distribution system of a networked service provider. | 02-04-2010 |
20100182322 | System and Method for Generating Circular Layout Graphs - A system and method for identifying a plurality of nodes for a circular layout, each of the plurality of nodes to be connected via an edge to at least one other of the plurality of nodes, fixing all of the plurality of nodes on a unit circle, except for one of the nodes, moving the unfixed node to a barycenter of the fixed plurality of nodes, projecting the unfixed node to the unit circle of the circular layout and displaying the circular layout. | 07-22-2010 |
20110044197 | METHOD AND APPARATUS FOR MEASURING AND EXTRACTING PROXIMITY IN NETWORKS - A method and apparatus for measuring and extracting proximity in networks are disclosed. In one embodiment, the present method receives a network from a user for analysis and extraction of a smaller proximity sub-graph. The method computes a candidate sub-graph and determines at least one Cycle Free Escape Conductivity (CFEC) proximity of at least two nodes in accordance with the candidate sub-graph. The method then extracts and presents a proximity sub-graph that best captures the proximity. | 02-24-2011 |
20110153663 | RECOMMENDATION ENGINE USING IMPLICIT FEEDBACK OBSERVATIONS - Systems and methods to provide a recommendation engine that uses implicit feedback observations are provided. A particular method includes receiving accessing data comprising a plurality of implicit feedback observations for a plurality of users. The plurality of users includes a first user that requested a recommendation. Each implicit feedback observation is associated with a particular user and a particular item of a plurality of items. The method includes determining a plurality of preference ratings and a plurality of confidence ratings for each user of the plurality of users for each item based on the plurality of implicit feedback observations. The method includes generating a recommendation list of one or more of the plurality of items for the first user based on the plurality of preference ratings and the plurality of confidence ratings. | 06-23-2011 |
20120253884 | METHODS AND APPARATUS FOR MODELING RELATIONSHIPS AT MULTIPLE SCALES IN RATINGS ESTIMATION - Systems and techniques for generating item ratings for a user in order to allow for recommendations of selected items for that user. A set of known ratings of different items for a plurality of users is collected and maintained, and these known ratings are used to estimate rating factors influencing ratings, including user and item factors. Initial user and item factors are estimated and new user and item factors are successively added, with the original rating factors being progressively shrunk so as to reduce their magnitude and their contribution to the rating estimation as successive factors are added. When an appropriate number of user and item factors has been estimated, the rating factors are used to estimate ratings of items for a user, and the estimated ratings are employed to generate recommendations for that user. | 10-04-2012 |