1. A method comprising facilitating a processing of and/or processing (1) data and/or (2) information and/or (3) at least one signal, the (1) data and/or (2) information and/or (3) at least one signal based, at least in part, on the following: an ontology for specifying a hierarchy of one or more abstraction levels for items data used in latent factorization models; a generation, by at least one processor implemented in hardware, of one or more user models for the items data corresponding to each abstraction level of the ontology; a processing of at least one request for one or more recommendations (a) to determine a requested abstraction level, and (b) to determine a privacy level, a security level, or a combination thereof associated with the at least one request; a processing of the privacy level, the security level, or the combination thereof against one or more privacy policies, one or more security policies, or a combination thereof to determine permission to access the requested abstraction level; a generation, a retrieval, or a combination thereof of the at least one of the one or more user models based, at least in part, on whether the at least one of the one or more user models exists at the requested abstraction level, a selection of at least one of the one or more user models for generating the one or more recommendations for one or more applications, one or more services, or a combination thereof based, at least in part, on the one or more privacy policies, the one or more security policies, or the combination thereof; and wherein the one or more abstraction levels correspond to different levels of the privacy policies and the security policies of the one or more user models.
1. A method comprising facilitating a processing of and/or processing (1) data and/or (2) information and/or (3) at least one signal, the (1) data and/or (2) information and/or (3) at least one signal based, at least in part, on the following: an ontology for specifying a hierarchy of one or more abstraction levels for items data used in latent factorization models; a generation, by at least one processor implemented in hardware, of one or more user models for the items data corresponding to each abstraction level of the ontology; a processing of at least one request for one or more recommendations (a) to determine a requested abstraction level, and (b) to determine a privacy level, a security level, or a combination thereof associated with the at least one request; a processing of the privacy level, the security level, or the combination thereof against one or more privacy policies, one or more security policies, or a combination thereof to determine permission to access the requested abstraction level; a generation, a retrieval, or a combination thereof of the at least one of the one or more user models based, at least in part, on whether the at least one of the one or more user models exists at the requested abstraction level, a selection of at least one of the one or more user models for generating the one or more recommendations for one or more applications, one or more services, or a combination thereof based, at least in part, on the one or more privacy policies, the one or more security policies, or the combination thereof; and wherein the one or more abstraction levels correspond to different levels of the privacy policies and the security policies of the one or more user models. 2. A method of claim 1 , wherein the (1) data and/or (2) information and/or (3) at least one signal are further based, at least in part, on the following: user interaction information, ratings information, context information, or a combination thereof associated with the at least one device, the at least one user of the at least one device, or a combination thereof; and a processing of the user interaction information, the ratings information, the context information, or the combination based, at least in part, on the ontology to generate the one or more user models for the one or more abstraction levels.