Abstract: A method of managing feature distribution in content of a content catalog for a content distribution system having a plurality of users is provided. The method comprises taking corrective action responsive to determining that a number of content items in a content catalog tagged with a feature exceeds a threshold.

Abstract: A computer-implemented method for generating one or more content recommendation, the method comprising: opening a content recommendation session for a selected user, wherein, as part of the session, a content recommendation procedure for generating one or more recommendation candidates based on a seed content item or seed content item identifier is available; receiving a request representing one or more desired content attributes; performing the content recommendation procedure thereby to generate one or more content recommendation candidates for the desired content attributes.

Abstract: There is provided method of processing searches input into a content recommendation engine (CRE). The CRE is adapted to receive searches and provide one or more content recommendations based on the received searches for a user of a content distribution system having a plurality of users. The method comprises determining a time between consecutive searches of a plurality of searches; grouping one or more of the plurality of searches into a search session based on the determined time; grouping searches in the search session into a sub-session; and determining an intended search of the sub-session. In another embodiment, the method comprises determining a similarity of each search of a plurality of searches; and mapping each search of the plurality of searches to an intended search based on the determined similarity.

Abstract: A computer-implemented method of determining an affinity profile for a temporary and/or anonymous user of a content distribution system, comprises: monitoring activity of the temporary and/or anonymous user comprising identifying content selected for viewing by the user; obtaining metadata concerning the selected items of content, the metadata representing at least some properties of the selected items of content; generating a user record for the user, the user record comprising or representing the user activity and/or the associated content metadata; processing the user record to generate an affinity profile for the user based on the user record, the affinity profile comprising at least one selected affinity category or affinity category score, wherein the affinity categories are selected from a stored set of affinity categories, each representing a user's affinity for a respective subject area.

Abstract: A computer-implemented method for obtaining one or more recommendation candidates for items of content available via a content distribution system, the method comprising: obtaining user data for a selected user, wherein the user data comprise or represent content metadata associated with user activity; performing a content recommendation process using the user data and content information for the available content to generate one or more initial content recommendation candidates for the user; generating or otherwise obtaining relationship information for the available content items and/or metadata associated with the available content items; generating one or more further content recommendation candidates from the initial content recommendation candidates using the relationship information.

Abstract: A computer-implemented method of using affinity profiles to determine at least one audience category that is expected to consume a selected item of content at one or more selected time periods, the method comprises: obtaining affinity profiles for a plurality of users of a content distribution system, wherein each affinity profile comprises at least one selected affinity category or affinity category score, and the affinity categories are selected from a stored set of affinity categories, each representing a user's affinity for a respective subject area; monitoring consumption of items of content or other activity of the users for different time periods; determine the likelihood of, or an expected audience measure for, the selected item of content being consumed during the or each selected time period based on the affinity profiles and the monitored consumption of content or other activity for the different time periods.

Abstract: A computer-implemented method of determining an affinity profile for a temporary and/or anonymous user of a content distribution system, comprises: monitoring activity of the temporary and/or anonymous user comprising identifying content selected for viewing by the user; obtaining metadata concerning the selected items of content, the metadata representing at least some properties of the selected items of content; generating a user record for the user, the user record comprising or representing the user activity and/or the associated content metadata; processing the user record to generate an affinity profile for the user based on the user record, the affinity profile comprising at least one selected affinity category or affinity category score, wherein the affinity categories are selected from a stored set of affinity categories, each representing a user's affinity for a respective subject area.

Abstract: A computer-implemented method of determining affinity profiles, comprises: for each of a plurality of user devices, monitoring user activity including identifying content selected for viewing by the user of the user device; obtaining metadata concerning the selected items of content, the metadata representing at least some properties of the selected items of content; generating or updating a user record for the user, the user record comprising or representing the user activity and/or the associated content metadata; processing the user record to generate an affinity profile for the user based on the user record, the affinity profile comprising at least one selected affinity category or affinity category score, wherein the affinity categories are selected from a stored set of affinity categories, each representing a user's affinity for a respective subject area.

Abstract: A computer-implemented method for generating text data comprising a descriptor for a group of content of items, the method comprising: obtaining content metadata associated with a group of content items; generating a descriptor for the group of content items based on at least the content metadata associated with the group; at least one of displaying and storing the generated descriptor.

Abstract: A computer-implemented method and system for generating a synopsis for a content item comprising: obtaining user data for at least one user and/or associated metadata, wherein the user data and/or associated metadata is based on user activity for the at least one user; receiving content metadata and/or other content information for the content item; generating or otherwise obtaining a synopsis for the content item based on the obtained content information and based on at least some of said obtained user data.

Abstract: A content selection interface configuration system for configuring a content selection interface allowing user selection of content presented for viewing, the content selection interface configuration system configured to: provide one or more groups of content, each group of content including a plurality of content items; obtain data indicating at least one property of the content items of each group of content; obtain user activity including interactions by the user with content on a content selection interface; and determine a score each group of content of the plurality of groups of content based at least in part on the user activity of that user and the data indicating the at least one property of some or all of the content, wherein the score is indicative of a likely interest of the content in that group of content to one or more users of the content selection interface.

Abstract: A computer-implemented method of using affinity profiles to determine at least one audience category that is expected to consume a selected content item type, the method comprises: obtaining affinity profiles for a plurality of users of a content distribution system, wherein each affinity profile comprises at least one selected affinity category or affinity category score, and the affinity categories are selected from a stored set of affinity categories, each representing a user's affinity for a respective subject area; monitoring consumption of selected items of content of the selected type to determine which of the users consumed the items of content of the selected type; determine an audience that is expected to consume the items of content of the selected type based on the affinity profiles of the users that were determined to have consumed the selected items of content.

Abstract: A computer-implemented method and system for creating a channel schedule for a linear broadcasting content provider, is disclosed. The method comprises: obtaining metadata associated with a plurality of content items in a content catalogue, the metadata representing at least some properties of the content items; segmenting the plurality of content items into segments based on metadata similarity; creating one or more channels based on the segments; and scheduling the plurality of content items, in the respective segments, for the one or more channels.

Abstract: A computer-implemented method for determining desirable content for a content distribution system is disclosed. The method comprises: for each of a plurality of user devices, monitoring user activity including identifying content that the user of the user device has interacted with; obtaining metadata concerning the identified items of content, the metadata representing at least some properties of the identified items of content; aggregating the metadata for a plurality of users; using the aggregated metadata to identify one or more items or combinations of metadata popular with the plurality of users; and using the identified metadata to determine desirable content.

Abstract: A method of managing storage of user data of a content recommendation system is provided. The content recommendation system comprises a user learning module for receiving data indicative of user actions, determining one or more learn actions based on the received data and storing the learn actions on a storage resource. The method comprises determining a parameter associated with aging out a learn action stored on a storage resource of a content recommendation system. The learn action determined based on data indicative of a user action. The method further comprises aging out the learn action stored on the storage resource based on the parameter.

Abstract: A computer-implemented method comprising: obtaining user data for a user, wherein the user data comprises or represents user activity and/or content metadata associated with user activity; obtaining metadata and/or one or more other properties associated with a plurality of visual indications associated with a content item; selecting one or more of the plurality of visual indications based on at least the user data and the visual indication metadata and/or the one or more other properties of the visual indication; displaying the selected one or more visual indications, for example, as part of a content selection interface.

Abstract: A computer-implemented method for generating a descriptive persona text for one or more users of a content recommendation system comprising: obtaining user data and/or associated content metadata for the one or more users, wherein the user data and/or associated content metadata is based on user activity of the one or more users; generating the descriptive persona text for the one or more users based on the user data and/or associated content metadata, wherein the generated descriptive persona text comprises at least a persona title and/or a persona description.

Abstract: A computer-implemented method and system for creating a virtual channel based on linear broadcast content, is disclosed. The method comprises obtaining scheduling information and content information associated with a plurality of content items in a content schedule for two or more existing channels, the content information comprising at least an identifier for each content item; determining, for each timeslot in a predetermined time period, a ranking for each content item scheduled in each timeslot; selecting, for each timeslot, a content item based on the ranking; and creating one or more virtual channel schedules comprising at least the identifier for the selected content items from the two or more existing channels.

Abstract: A computer-implemented method of determining affinity profiles, comprises: for each of a plurality of user devices, monitoring user activity including identifying television content selected for viewing by the user of the user device; obtaining metadata concerning the selected items of television content, the metadata representing at least some properties of the selected items of television content; generating or updating a user record for the user, the user record comprising or representing the user activity and/or the associated content metadata; processing the user record to generate an affinity profile for the user based on the user record, the affinity profile comprising at least one selected affinity category or affinity category score, wherein the affinity categories are selected from a stored set of affinity categories, each representing a user's affinity for a respective subject area.

Abstract: A computer-implemented method of targeting users based on content consumption comprises: for each of a plurality of users, monitoring user activity including identifying content selected for viewing by the users; obtaining metadata concerning the selected items of content, the metadata representing at least some properties of the selected items of content; generating or updating a user record for each user, the user record comprising or representing the user activity and/or the associated content metadata; and identifying users that have below a threshold level of user activity.