Abstract: A computer-implemented method for canine mobility detection is disclosed. The method includes processing mobility data captured by a device attached to a canine, analyzing canine data corresponding to the canine to determine at least one baseline canine, wherein the at least one baseline canine is similar to the canine, for each of the one or more metrics, comparing the one or more metrics of the canine to one or more baseline metrics of the at least one baseline canine, determining one or more scores for each of the one or more metrics, the one or more scores based on a normal range of the one or more metrics from the one or more baseline metrics of the at least one baseline canine, and displaying at least one alert on one or more user interfaces of a user device.

Abstract: Methods, apparatuses, and computer program products are described herein. One example embodiment may include a method for providing a hybrid ranked list of items to a user device including receiving at least one of criteria input or an input weight, receiving an indication of peer recommendation of the one or more items correlated to the at least one of the criteria input or the input weight, correlating the at least one of the criteria input or the input weight to one or more normalized values, determining the one or more items according to a weighted value, receiving the one or more items determined based on the one or more normalized values, generating the hybrid ranked list of items, and providing, to the user device, the hybrid ranked list of items including the one or more items correlated to the at least one of the criteria input or the input weight.

Abstract: Methods, apparatuses, and computer program products are described herein that are configured for creating a hybrid recommendation algorithm that programmatically decreases the importance of the content-based data as the basis of affinity data supporting collaborative filtering grows over time. One example embodiment may include a method for computing a content based similarity metric between a first item and a second item, accessing each of one or more instances of affinity data; and calculating an overall similarity metric between the first item and the second item, the overall similarity metric being a function of the content based similarity metric between the first item and the second item and a number of instances of empirical data for the items, the function defined such that as the number of instances of empirical data increases, the overall similarity metric increases a relative contribution in favor of the empirical data.

Abstract: Methods, apparatuses, and computer program products are described herein that are configured to programmatically synthesize multiple five different sources of data bearing on user preferences for providing a recommendation of an item in response to a recommendation request. One example embodiment may include a method for receiving a recommendation request, providing a search result, the search results generated by querying a set of (target user, affinity) pairs for items matching the search terms and sorting the matching items according to a combination of the affinities and a strength of text matching.