Abstract: A computer-implemented method of triggering an action based on behaviour of a user consuming content at a user device, the method comprising receiving content change signals at a processor executing a content change detection algorithm, wherein the content change signals denote a change of content on the user device; wherein the detection algorithm compares the content change signals with a content change behaviour associated with that device to detect a surfing condition and generates a triggering control signal for triggering an action when the surfing condition for that device is detected.

Abstract: A content delivery system delivers media content to a content consuming user and comprises computer storage storing a media asset; an analysis component configured to perform an analysis of the user's behavior at a user device to infer a desirable attribute of a modified version of the media asset, the user's behavior comprising the user instigating at the user device requests for content other than the media asset to a computer network, and consuming the requested other content when returned from the network. The requests do not explicitly identify the desirable attribute. The system comprises a modified version generation component configured to receive the media asset and modify the media asset based on the analysis of the user's behavior; and a content delivery component configured to supply the modified version of the media asset to a media output device of the user, the modified version exhibiting the desirable attribute.

Abstract: In the following, a content delivery system delivers a modified version of a media asset to a current content consuming user. Control information identifying a desired attribute of the modified version of the asset is received from the current content consuming user. The media asset is modified based on the control information and audience reaction data associated with the media asset and generated by analyzing at least a previous content consuming user's reactions to the media asset while the media asset was supplied to a media output device of the previous content consuming user.

Abstract: In the following, a content delivery system delivers a modified version of a media asset to a current content consuming user. Control information identifying a desired attribute of the modified version of the asset is received from the current content consuming user. The media asset is modified based on the control information and audience reaction data associated with the media asset and generated by analyzing at least a previous content consuming user's reactions to the media asset whilst the media asset was supplied to a media output device of the previous content consuming user.

Abstract: A search technology generates recommendations with minimal user data and participation, and provides better interpretation of user data, such as popularity, thus obtaining breadth and quality in recommendations. It is sensitive to the semantic content of natural language terms and lets users briefly describe the intended recipient (i.e., interests, eccentricities, previously successful gifts). Based on that input, the recommendation software system and method determines the meaning of the entered terms and creatively discover connections to gift recommendations from the vast array of possibilities. The user may then make a selection from these recommendations. The search/recommendation engine allows the user to find gifts through connections that are not limited to previously available information on the Internet. Thus, interests can be connected to buying behavior by relating terms to respective items.

Abstract: In the following, a content delivery system delivers a modified version of a media asset to a current content consuming user. Control information identifying a desired attribute of the modified version of the asset is received from the current content consuming user. The media asset is modified based on the control information and audience reaction data associated with the media asset and generated by analyzing at least a previous content consuming user's reactions to the media asset whilst the media asset was supplied to a media output device of the previous content consuming user.

Abstract: A computer-implemented method of triggering an action based on behaviour of a user consuming content at a user device, the method comprising receiving content change signals at a processor executing a content change detection algorithm, wherein the content change signals denote a change of content on the user device; wherein the detection algorithm compares the content change signals with a content change behaviour associated with that device to detect a surfing condition and generates a triggering control signal for triggering an action when the surfing condition for that device is detected.

Abstract: A search technology generates recommendations with minimal user data and participation, and provides better interpretation of user data, such as popularity, thus obtaining breadth and quality in recommendations. It is sensitive to the semantic content of natural language terms and lets users briefly describe the intended recipient (i.e., interests, eccentricities, previously successful gifts). Based on that input, the recommendation software system and method determines the meaning of the entered terms and creatively discover connections to gift recommendations from the vast array of possibilities. The user may then make a selection from these recommendations. The search/recommendation engine allows the user to find gifts through connections that are not limited to previously available information on the Internet. Thus, interests can be connected to buying behavior by relating terms to respective items.

Abstract: A content delivery system delivers media content to a content consuming user and comprises computer storage storing a media asset; an analysis component configured to perform an analysis of the user's behavior at a user device to infer a desirable attribute of a modified version of the media asset, the user's behavior comprising the user instigating at the user device requests for content other than the media asset to a computer network, and consuming the requested other content when returned from the network. The requests do not explicitly identify the desirable attribute. The system comprises a modified version generation component configured to receive the media asset and modify the media asset based on the analysis of the user's behavior; and a content delivery component configured to supply the modified version of the media asset to a media output device of the user, the modified version exhibiting the desirable attribute.

Abstract: In the following, a content delivery system delivers a modified version of a media asset to a current content consuming user. Control information identifying a desired attribute of the modified version of the asset is received from the current content consuming user. The media asset is modified based on the control information and audience reaction data associated with the media asset and generated by analyzing at least a previous content consuming user's reactions to the media asset whilst the media asset was supplied to a media output device of the previous content consuming user.

Abstract: A computer implemented method of controlling access to content to be viewed on a viewing device, the method comprising: selecting desired content to be viewed on the viewing device; displaying auxiliary content which is required to be viewed while preventing display of the desired content on the viewing device; after displaying the auxiliary content, displaying at least one user engagement element defining an expected user activity; detecting if a user has engaged with the user engagement element in accordance with the expected user activity and if so, displaying the desired content on the viewing device.

Abstract: A search technology generates recommendations with minimal user data and participation, and provides better interpretation of user data, such as popularity, thus obtaining breadth and quality in recommendations. It is sensitive to the semantic content of natural language terms and lets users briefly describe the intended recipient (i.e., interests, eccentricities, previously successful gifts). Based on that input, the recommendation software system and method determines the meaning of the entered terms and creatively discover connections to gift recommendations from the vast array of possibilities. The user may then make a selection from these recommendations. The search/recommendation engine allows the user to find gifts through connections that are not limited to previously available information on the Internet. Thus, interests can be connected to buying behavior by relating terms to respective items.

Abstract: A search technology generates recommendations with minimal user data and participation, and provides better interpretation of user data, such as popularity, thus obtaining breadth and quality in recommendations. It is sensitive to the semantic content of natural language terms taken from user profiles, which can include interests, eccentricities, age, gender, and location information associated with the user. The interest information can include music, movies, sports and personality traits. Based on the user's profile information, the system determines which ad from a stock of ads is best suited to a given profile and delivers that ad. The system can be used to match user profiles to provide mate-matching.

Abstract: A search technology generates recommendations with minimal user data and participation, and provides better interpretation of user data, such as popularity, thus obtaining breadth and quality in recommendations. It is sensitive to the semantic content of natural language terms taken from user profiles, which can include interests, eccentricities, age, gender, and location information associated with the user. The interest information can include music, movies, sports and personality traits. Based on the user's profile information, the system determines which ad from a stock of ads is best suited to a given profile and delivers that ad. A user interface can be provided to enable the user to fine-tune product and service recommendation results.

Abstract: A search technology generates recommendations with minimal user data and participation, and provides better interpretation of user data, such as popularity, thus obtaining breadth and quality in recommendations. It is sensitive to the semantic content of natural language terms and lets users briefly describe the intended recipient (i.e., interests, eccentricities, previously successful gifts). Based on that input, the recommendation software system and method determines the meaning of the entered terms and creatively discover connections to gift recommendations from the vast array of possibilities. The user may then make a selection from these recommendations. The search/recommendation engine allows the user to find gifts through connections that are not limited to previously available information on the Internet. Thus, interests can be connected to buying behavior by relating terms to respective items.

Abstract: A search technology generates recommendations with minimal user data and participation, and provides better interpretation of user data, such as popularity, thus obtaining breadth and quality in recommendations. It is sensitive to the semantic content of natural language terms taken from user profiles at social networking and online dating applications and blogs. The profiles and blogs can include interests, eccentricities, age, gender, and location information associated with the user. The interest information can include music, movies, sports and personality traits. Based on the user's profile information, the system determines which ad from a stock of ads is best suited to a given profile and delivers that ad. The system can enable advertisers to create and manage online advertising campaigns using a campaign manager in which they attach descriptions to ads in their inventory, thereby generating a profile for each ad which is then compared to the profiles in the target online environment.

Abstract: A search technology generates recommendations with minimal user data and participation, and provides better interpretation of user data, such as popularity, thus obtaining breadth and quality in recommendations. It is sensitive to the semantic content of natural language terms and lets users briefly describe the intended recipient (i.e., interests, eccentricities, previously successful gifts). Based on that input, the recommendation software system and method determines the meaning of the entered terms and creatively discover connections to gift recommendations from the vast array of possibilities. The user may then make a selection from these recommendations. The search/recommendation engine allows the user to find gifts through connections that are not limited to previously available information on the Internet. Thus, interests can be connected to buying behavior by relating terms to respective items.

Abstract: A search technology generates recommendations with minimal user data and participation, and provides better interpretation of user data, such as popularity, thus obtaining breadth and quality in recommendations. It is sensitive to the semantic content of natural language terms and lets users briefly describe the intended recipient (i.e., interests, eccentricities, previously successful gifts). Based on that input, the recommendation software system and method determines the meaning of the entered terms and creatively discover connections to gift recommendations from the vast array of possibilities. The user may then make a selection from these recommendations. The search/recommendation engine allows the user to find gifts through connections that are not limited to previously available information on the Internet. Thus, interests can be connected to buying behavior by relating terms to respective items.

Abstract: A search technology generates recommendations with minimal user data and participation, and provides better interpretation of user data, such as popularity, thus obtaining breadth and quality in recommendations. It is sensitive to the semantic content of natural language terms taken from user profiles at social networking and online dating applications and blogs. The profiles and blogs can include interests, eccentricities, age, gender, and location information associated with the user. The interest information can include music, movies, sports and personality traits. Based on the user's profile information, the system determines which ad from a stock of ads is best suited to a given profile and delivers that ad. The system can enable advertisers to create and manage online advertising campaigns using a campaign manager in which they attach descriptions to ads in their inventory, thereby generating a profile for each ad which is then compared to the profiles in the target online environment.

Abstract: A search technology generates recommendations with minimal user data and participation, and provides better interpretation of user data, such as popularity, thus obtaining breadth and quality in recommendations. It is sensitive to the semantic content of natural language terms and lets users briefly describe the intended recipient (i.e., interests, eccentricities, previously successful gifts). Based on that input, the recommendation software system and method determines the meaning of the entered terms and creatively discover connections to gift recommendations from the vast array of possibilities. The user may then make a selection from these recommendations. The search/recommendation engine allows the user to find gifts through connections that are not limited to previously available information on the Internet. Thus, interests can be connected to buying behavior by relating terms to respective items.