Abstract: Systems, methods, and articles of manufacture are disclosed for learning models of movies, keywords, actors, and roles, and querying the same. In one embodiment, a recommendation application optimizes a model based on training data by initializing the mean and co-variance matrices of Gaussian distributions representing movies, keywords, and actors to random values, and then performing an optimization to minimize a margin loss function using symmetrical or asymmetrical measures of similarity between entities. Such training produces an optimized model with the Gaussian distributions representing movies, keywords, and actors, as well as shift vectors that change the means of movie Gaussian distributions and model archetypical roles. Subsequent to training, the same similarity measures used to train the model are used to query the model and obtain rankings of entities based on similarity to terms in the query, and a representation of the rankings may be displayed via, e.g., a display device.

Abstract: Systems and methods are disclosed for optimizing full-spectrum cardinality approximations on big data utilizing an optimized order statistics technique. To accomplish the foregoing, a multiset of objects that each corresponds to one of a plurality of objects associated with a resource are obtained. A compound data object is populated at least in part with data that is derived based on generated decimal fraction hash values that correspond to each object in the obtained multiset. The populated compound data object is processed with a full-spectrum arithmetic mean estimation operation that can accurately determine a cardinality estimate for the obtained multiset using less resources and time when compared to traditional techniques. The determination is further made without the need to employ linear counting or bias correction operations on low or high cardinalities.

Abstract: Systems and methods are disclosed for optimizing full-spectrum cardinality approximations on big data utilizing an optimized LogLog counting technique. To accomplish the foregoing, a multiset of objects that each corresponds to one of a plurality of objects associated with a resource are obtained. A compound data object is populated at least in part with data that is derived based on generated hash values that correspond to each object in the obtained multiset. The populated compound data object is processed with a full-spectrum harmonic mean estimation operation that can accurately determine a cardinality estimate for the obtained multiset using less resources and time when compared to traditional techniques. The determination is further made without the need to employ linear counting or bias correction operations on low or high cardinalities.

Abstract: Systems and methods are disclosed for optimizing full-spectrum cardinality approximations on big data utilizing an optimized LogLog counting technique. To accomplish the foregoing, a multiset of objects that each corresponds to one of a plurality of objects associated with a resource are obtained. A compound data object is populated at least in part with data that is derived based on generated hash values that correspond to each object in the obtained multiset. The populated compound data object is processed with a full-spectrum harmonic mean estimation operation that can accurately determine a cardinality estimate for the obtained multiset using less resources and time when compared to traditional techniques. The determination is further made without the need to employ linear counting or bias correction operations on low or high cardinalities.

Abstract: Systems and methods are disclosed for optimizing full-spectrum cardinality approximations on big data utilizing an optimized order statistics technique. To accomplish the foregoing, a multiset of objects that each corresponds to one of a plurality of objects associated with a resource are obtained. A compound data object is populated at least in part with data that is derived based on generated decimal fraction hash values that correspond to each object in the obtained multiset. The populated compound data object is processed with a full-spectrum arithmetic mean estimation operation that can accurately determine a cardinality estimate for the obtained multiset using less resources and time when compared to traditional techniques. The determination is further made without the need to employ linear counting or bias correction operations on low or high cardinalities.