Rating models may be generated by obtaining a plurality of consumption values, obtaining a plurality of rating values, training a model that estimates consumption values and rating values by utilizing a plurality of consumer attributes for each consumer, a plurality of item attributes for each item, and a plurality of weights for each attribute of each combination of a consumer and an item. Each estimated consumption value is a function of the plurality of weights for each attribute of each combination of each consumer and each item that corresponds with the estimated consumption value, and each estimated rating value is a function of the plurality of consumer attributes of a consumer that corresponds with the estimated rating value, the plurality of item attributes of an item that corresponds with the estimated rating value, and the plurality of weights that corresponds with the estimated rating value.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An apparatus comprising: a processor; and one or more computer readable mediums collectively including instructions that, when executed by the processor, cause the processor to: obtain a plurality of consumption values, wherein each consumption value indicates whether a consumer among a plurality of consumers has rated an item among a plurality of items; obtain a plurality of rating values, wherein each rating value represents a rating of an item by a consumer for each consumption value indicating that the corresponding consumer rated the corresponding item; and train a model that estimates consumption values and rating values by utilizing a plurality of consumer attributes for each consumer, a plurality of item attributes for each item, and a plurality of weights for each attribute of each combination of a consumer and an item, wherein each estimated consumption value is a function of the plurality of weights for each attribute of each combination of each consumer and each item that corresponds with the estimated consumption value, and each estimated rating value is a function of the plurality of consumer attributes of a consumer that corresponds with the estimated rating value, the plurality of item attributes of an item that corresponds with the estimated rating value, and the plurality of weights that corresponds with the estimated rating value. 2. The apparatus of claim 1, wherein the plurality of weights is based on: a plurality of consumer weights of a consumer that corresponds with the plurality of weights; and a plurality of item weights of an item that corresponds with the plurality of weights. 3. The apparatus of claim 2, wherein a weight of the plurality of weights is based on a product of a consumer weight and an item weight that corresponds with the weight. 4. The apparatus of claim 3, wherein an estimated rating value of an item and a consumer is based on a sum of products of a consumer attribute, a consumer weight, an item attribute, and an item weight for each attribute that corresponds with the item and the consumer. 5. The apparatus of claim 4, wherein the training of a model includes minimizing a loss function by updating a plurality of consumer attributes of each consumer, a plurality of item attributes of each item, and a plurality of weights for each attribute of each combination of a consumer and an item, wherein the loss function is based on a difference between the consumption data and the estimated consumption data and a difference between the rating data and the estimated rating data. 6. The apparatus of claim 5, wherein the training of a model minimizes the loss function by using a stochastic gradient descent method. 7. The apparatus of claim 1, wherein the instructions further cause the processor to: generate a recommendation of an item to a consumer based on the estimated rating values of the consumer and each item in the estimated rating data. 8. The apparatus of claim 7, wherein the obtaining of the plurality of rating values includes receiving a rating value from a consumer who bought an item from an Electronic Commerce (EC) site, and the obtaining of the plurality of consumption values includes updating a consumption value in response to receiving the rating value that corresponds with the consumption value. 9. The apparatus of claim 1, wherein the instructions further cause the processor to: generate a recommendation of an item based on a sum of the estimated rating values of each corresponding item. 10. The apparatus of claim 1, wherein the instructions further cause the processor to: generate an item that maximizes a rating value or a sum of rating values of one or more consumers based on the plurality of item attributes of each item and rating values that correspond with each item, wherein each item is data to be presented to a consumer. 11. A method comprising: obtaining a plurality of consumption values, wherein each consumption value indicates whether a consumer among a plurality of consumers has rated an item among a plurality of items; obtaining a plurality of rating values, wherein each rating value represents a rating of an item by a consumer for each consumption value indicating that the corresponding consumer rated the corresponding item; and training, using a processor, a model that estimates consumption values and rating values by utilizing a plurality of consumer attributes for each consumer, a plurality of item attributes for each item, and a plurality of weights for each attribute of each combination of a consumer and an item, wherein each estimated consumption value is a function of the plurality of weights for each attribute of each combination of each consumer and each item that corresponds with the estimated consumption value, and each estimated rating value is a function of the plurality of consumer attributes of a consumer that corresponds with the estimated rating value, the plurality of item attributes of an item that corresponds with the estimated rating value, and the plurality of weights that corresponds with the estimated rating value. 12. The method of claim 11, wherein the plurality of weights is based on: a plurality of consumer weights of a consumer that corresponds with the plurality of weights; and a plurality of item weights of an item that corresponds with the plurality of weights. 13. The method of claim 12, wherein a weight of the plurality of weights is based on a product of a consumer weight and an item weight that corresponds with the weight. 14. The method of claim 13, wherein an estimated rating value of an item and a consumer is based on a sum of products of a consumer attribute, a consumer weight, an item attribute, and an item weight for each attribute that corresponds with the item and the consumer. 15. The method of claim 14, wherein the training of a model includes minimizing a loss function by updating a plurality of consumer attributes of each consumer, a plurality of item attributes of each item, and a plurality of weights for each attribute of each combination of a consumer and an item, wherein the loss function is based on a difference between the consumption data and the estimated consumption data and a difference between the rating data and the estimated rating data. 16. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform operations comprising: obtaining a plurality of consumption values, wherein each consumption value indicates whether a consumer among a plurality of consumers has rated an item among a plurality of items; obtaining a plurality of rating values, wherein each rating value represents a rating of an item by a consumer for each consumption value indicating that the corresponding consumer rated the corresponding item; and training a model that estimates consumption values and rating values by utilizing a plurality of consumer attributes for each consumer, a plurality of item attributes for each item, and a plurality of weights for each attribute of each combination of a consumer and an item, wherein each estimated consumption value is a function of the plurality of weights for each attribute of each combination of each consumer and each item that corresponds with the estimated consumption value, and each estimated rating value is a function of the plurality of consumer attributes of a consumer that corresponds with the estimated rating value, the plurality of item attributes of an item that corresponds with the estimated rating value, and the plurality of weights that corresponds with the estimated rating value. 17. The computer program product of claim 16, wherein the plurality of weights is based on: a plurality of consumer weights of a consumer that corresponds with the plurality of weights; and a plurality of item weights of an item that corresponds with the plurality of weights. 18. The computer program product of claim 17, wherein a weight of the plurality of weights is based on a product of a consumer weight and an item weight that corresponds with the weight. 19. The computer program product of claim 18, wherein an estimated rating value of an item and a consumer is based on a sum of products of a consumer attribute, a consumer weight, an item attribute, and an item weight for each attribute that corresponds with the item and the consumer. 20. The computer program product of claim 19, wherein the training of a model includes minimizing a loss function by updating a plurality of consumer attributes of each consumer, a plurality of item attributes of each item, and a plurality of weights for each attribute of each combination of a consumer and an item, wherein the loss function is based on a difference between the consumption data and the estimated consumption data and a difference between the rating data and the estimated rating data.