Abstract: This disclosure describes systems and methods for selecting and/or ranking web-based content predicted to have the greatest interest to individual users. In particular, articles are ranked in terms of predicted interest for different users. This is done by optimizing an interest model and in particular through a method of bilinear regression and Bayesian optimization. The interest model is populated with data regarding users, the articles, and historical interest trends that types of users have expressed towards types of articles.

Abstract: A content-providing method and system, including identifying a representative type cluster by clustering content related to behavioral data which represents a use history of a user, according to type of the content, mapping the representative type cluster to a time interval, and storing the representative type cluster and the time interval.

Abstract: A set of item-item affinities for a plurality of items is determined based on collaborative-filtering techniques. A set of an item's nearest neighbor items based on the set of item-item affinities is determined. A set of user feature-item affinities for the plurality of items and a set of user features is determined based on least squared regression. A set of a user feature's nearest neighbor items is determined based in part on the set of user feature-item affinities. Compatible affinity weights for nearest neighbor items of each item and each user feature are determined. Based on user features of a user and items a user has consumed, a set of nearest neighbor items are identified as a set of candidate items, and affinity scores of candidate items are determined. Based on the affinity scores, a candidate item from the set of candidate items is recommended to the user.

Abstract: Content display policies are evaluated using two kinds of methods. In the first kind of method, using information, collected in a “controlled” manner about user characteristics and content characteristics, truth models are generated. A simulator replays users' visits to the portal web page and simulates their interactions with content items on the page based on the truth models. Various metrics are used to compare different content item-selecting algorithms. In the second kind of method, no explicit truth models are built. Events from the controlled serving scheme are replayed in part or whole; content item-selection algorithms learn using the observed user activities. Metrics that measure the overall predictive error are used to compare different content-item selection algorithms. The data collected in a controlled fashion plays a key role in both the methods.

Abstract: Two items are determined to be similar to each not only based on previous actual user behavior, but also based on the observed relatedness of the characteristics of those two items. A first characteristic and a second characteristic are determined to have some affinity for each other if a high proportion of users who select items having the first characteristics also select items that have the second characteristic, and vice-versa. Two items having characteristics with high affinity for each other are determined to have some similarity to each other, even if very few or no users who selected one of those items ever selected the other of those items. A first item that is determined to be sufficiently similar to second item in this manner may be recommended to a user who has selected the second item as potentially also being of interest to that user.

Abstract: A set of item-item affinities for a plurality of items is determined based on collaborative-filtering techniques. A set of an item's nearest neighbor items based on the set of item-item affinities is determined. A set of user feature-item affinities for the plurality of items and a set of user features is determined based on least squared regression. A set of a user feature's nearest neighbor items is determined based in part on the set of user feature-item affinities. Compatible affinity weights for nearest neighbor items of each item and each user feature are determined and stored. Based on user features of a particular user and items a particular user has consumed, a set of nearest neighbor items comprising nearest neighbor items for user features of the user and items the user has consumed are identified as a set of candidate items, and affinity scores of candidate items are determined.

Abstract: A content-providing method and system, including identifying a representative type cluster by clustering content related to behavioral data which represents a use history of a user, according to type of the content, mapping the representative type cluster to a time interval, and storing the representative type cluster and the time interval.

Abstract: A method comprising receiving a search query to generate a search result of one or more media items and providing a personalized search rank of the one or more media items on the basis of a user profile and an item relevance for a given media item with regard to the query metadata associated with the search result is identified and used to identify at least one related media item. The at least one related media item is ranked on the basis of the user profile and the metadata.

Abstract: A method and a system are provided for recommending an ad (e.g., item) for a user. In one example, the system constructs one or more user profiles. Each user profile is represented by a user feature set including user attributes. The system constructs one or more item profiles. Each item profile is represented by an item feature set including item attributes. The system receives historical item ratings given by one or more users. The system then generates one or more preference scores by modeling at least one relationship among the user profiles, the item profiles and the historical item ratings.

Abstract: Two items are determined to be similar to each not only based on previous actual user behavior, but also based on the observed relatedness of the characteristics of those two items. A first characteristic and a second characteristic are determined to have some affinity for each other if a high proportion of users who select items having the first characteristics also select items that have the second characteristic, and vice-versa. Two items having characteristics with high affinity for each other are determined to have some similarity to each other, even if very few or no users who selected one of those items ever selected the other of those items. A first item that is determined to be sufficiently similar to second item in this manner may be recommended to a user who has selected the second item as potentially also being of interest to that user.

Abstract: A set of item-item affinities for a plurality of items is determined based on collaborative-filtering techniques. A set of an item's nearest neighbor items based on the set of item-item affinities is determined. A set of user feature-item affinities for the plurality of items and a set of user features is determined based on least squared regression. A set of a user feature's nearest neighbor items is determined based in part on the set of user feature-item affinities. Compatible affinity weights for nearest neighbor items of each item and each user feature are determined and stored. Based on user features of a particular user and items a particular user has consumed, a set of nearest neighbor items comprising nearest neighbor items for user features of the user and items the user has consumed are identified as a set of candidate items, and affinity scores of candidate items are determined.

Abstract: This disclosure describes systems and methods for selecting and/or ranking web-based content predicted to have the greatest interest to individual users. In particular, articles are ranked in terms of predicted interest for different users. This is done by optimizing an interest model and in particular through a method of bilinear regression and Bayesian optimization. The interest model is populated with data regarding users, the articles, and historical interest trends that types of users have expressed towards types of articles.

Abstract: Information with respect to users, items, and interactions between the users and items is collected. Each user is associated with a set of user features. Each item is associated with a set of item features. An expected score function is defined for each user-item pair, which represents an expected score a user assigns an item. An objective represents the difference between the expected score and the actual score a user assigns an item. The expected score function and the objective function share at least one common variable. The objective function is minimized to find best fit for some of the at least one common variable. Subsequently, the expected score function is used to calculate expected scores for individual users or clusters of users with respect to a set of items that have not received actual scores from the users. The set of items are ranked based on their expected scores.

Abstract: Content display policies are evaluated using two kinds of methods. In the first kind of method, using information, collected in a “controlled” manner about user characteristics and content characteristics, truth models are generated. A simulator replays users' visits to the portal web page and simulates their interactions with content items on the page based on the truth models. Various metrics are used to compare different content item-selecting algorithms. In the second kind of method, no explicit truth models are built. Events from the controlled serving scheme are replayed in part or whole; content item-selection algorithms learn using the observed user activities. Metrics that measure the overall predictive error are used to compare different content-item selection algorithms. The data collected in a controlled fashion plays a key role in both the methods.

Abstract: A method comprising receiving a search query to generate a search result of one or more media items and providing a personalized search rank of the one or more media items on the basis of a user profile and an item relevance for a given media item with regard to the query metadata associated with the search result is identified and used to identify at least one related media item. The at least one related media item is ranked on the basis of the user profile and the metadata.