Abstract:
To make it possible to enhance convenience for users and a seller of content. There is provided an information processing device including: a schema converting unit that converts content meta-information managed by a plurality of different management systems into a common schema; and a recommendation unit that determines a combination of content to be recommended and a user based on commonized content meta-information which is obtained by the conversion into the common schema and content purchase history information in the respective management systems.
Abstract:
PROBLEM TO BE SOLVED: To more accurately calculate a true spectral intensity on the basis of a measured spectrum.SOLUTION: Provided is an information processing apparatus including an estimation unit that expresses a light intensity distribution, which is obtained by irradiating light to a measurement object of a measurement target having a plurality of substances with mutually different responsive characteristics to the light on a surface of and/or inside it, as a linear combination of light intensity distributions obtained by irradiating the light to reference measurement objects, each of which has only one of the plurality of substances, models the light intensity distributions obtained from the respective reference measurement objects on the assumption that they obey predetermined probability distributions, and estimates combination coefficients of the linear combination from the light intensity distribution obtained from the measurement object of the measurement target.
Abstract:
PROBLEM TO BE SOLVED: To figure out more exactly the true spectral intensity on the basis of a measured spectrum.SOLUTION: An information processing apparatus comprises: an assaying unit that statistically assays each frequency band by using, as objects of comparison, simple-stained data obtained by fluorescence measuring of particles simply stained with a staining substance having prescribed fluorescence characteristics and non-stained data obtained by fluorescence measuring of unstained particles; a masking unit that sets, for each frequency band, in the absence of any significant difference between the simple-stained data and the non-stained data, the simple-stained data to 0 or a prescribed value; and an estimating unit that estimates, on the basis of the assumption that double-stained data obtained by fluorescence measuring of particles stained with a plurality of staining substances is expressed by a linear combination of base vectors representing the distribution of pieces of simple-stained data each corresponding to each staining substance, the combination coefficient of the pertinent linear combination.
Abstract:
PROBLEM TO BE SOLVED: To provide an evaluation prediction device capable of predicting an evaluation value at high speed.SOLUTION: An evaluation prediction device comprises: a posterior distribution calculation part for calculating variational posterior distributions of a first potential vector showing a potential feature of a first item and a second potential vector showing a potential feature of a second item, by regarding each residual matrix Rh of rank h (h=0 to H) in an evaluation value matrix of a rank quantity H that, as factor, has an evaluation value expressed by the first potential vector, the second potential vector and an inner product of the first and second potential vectors, as a random variable following a normal distribution, and by executing a variable Bayesian estimate using a known evaluation value provided as learning data; and an evaluation value prediction part for predicting the evaluation value that is unknown yet, using the variable posterior distribution of the first and second potential vectors that is calculated by the posterior distribution calculation part.
Abstract:
PROBLEM TO BE SOLVED: To further enhance recommendation precision.SOLUTION: An evaluation prediction device is provided which defines first and second latent vectors respectively corresponding to first and second items, an evaluation value expressed by an internal product of the first and the second latent vectors corresponding to each of the combinations of the first and the second items, first and second characterizing vectors respectively corresponding to the first and the second items, first and second projection matrix for projecting the first and the second characterizing vectors in space of the first and the second latent vectors. According to a normal distribution with the projection value of the first and the second characterizing vectors through the first and the second projection matrix as an expected value, the first and the second latent vectors are expressed. A Bayesian estimation is performed by using the first characterizing vector, the second characterizing vector and an existing evaluation value as study data, thereby calculating the posterior distribution of the parameter groups. Based on the posterior distribution of the parameter groups, the distribution of unknown evaluation values is calculated.