Abstract: A recommendation process includes the consideration of the compatibility of the items being recommended. An electronic processing system for generating a compatibility-aware recommendation output set to a is user based, at least in part, on a set of item compatibility rules. The system includes a processing system of one or more processors configured to receive applicable data, including i) user preference data, and ii) item compatibility rules, and to produce a compatibility-aware recommendation output set using the user preference data and the item compatibility rules. A method and computer-readable storage device containing the method are also presented.

Abstract: The invention includes an electronic processing system, a method and a computer readable storage device for generating a serendipity-weighted recommendation output set to a user based, at least in part, on a serendipity function. The system includes a processing system to receive user item preference data and community item popularity data. The processing system is also configured to produce an item recommendation set from the user item preference data, produce a set of item serendipity control values in response to the serendipity function and the community item popularity data, and combine the item recommendation set with the set of item serendipity control values to produce a serendipity-weighted and filtered recommendation output set. The method includes receiving item preference data and community item popularity data.

Abstract: The invention includes a system, method and article of manufacture for generating a serendipity-weighted recommendation output set to a user based, at least in part, on a serendipity function. The system includes a processing system of one or more processors configured to receive applicable data that includes item recommendation data and community item popularity data. The processing system is also configured to produce a set of item serendipity control values in response to the serendipity function and the community item popularity data, and to combine the item recommendation data with the set of item serendipity control values to produce a serendipity-weighted and filtered recommendation output set. The method includes receiving applicable data by the processing system, including inputting item recommendation data and community item popularity data.