Information processing apparatus, information processing method, and non-transitory computer readable recording medium
AssigneeYahoo Japan Corporation
InventorYukihiro Tagami
Life of this patent
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Abstract
An information processing apparatus according to the present application includes a classification unit, a conversion unit, a label acquiring unit. The classification unit that classifies a feature vector by using a classification vector, the feature vector being converted from target data. The conversion unit that converts the feature vector into an embedding vector in accordance with a conversion rule according to classification performed by the classification unit. The label acquiring unit that acquires, as one or more labels to be provided to the target data, one or more labels acquired based on the embedding vector converted by the conversion unit.
Claims10
An information processing apparatus comprising:
- a processor programmed to:
- classify a feature vector by using a classification vector, the feature vector being converted from user inputted target data, which includes at least one of a search query and a browsing activity of the user;
- convert the classified feature vector into an embedding vector in accordance with a conversion rule;
- acquire, as one or more labels to be provided to the target data, one or more labels acquired based on the converted embedding vector;
- acquire (i) a first feature vector corresponding to a first label vector and
- (ii) a predetermined number of second feature vectors corresponding to a predetermined number of vectors of second label vectors, values of inner products between the first label vector and the predetermined number of second label vectors being upper-order values;
- and
- learn the classification vector by using the first feature vector and the predetermined number of second feature vectors as learning data.
An information processing apparatus comprising:
- a processor programmed to:
- classify a feature vector by using a classification vector, the feature vector being converted from user inputted target data, which includes at least one of a search query and a browsing activity of the user;
- convert the classified feature vector into an embedding vector in accordance with a conversion rule;
- acquire, as one or more labels to be provided to the target data, one or more labels acquired based on the converted embedding vector;
- acquire (i) a first embedding vector corresponding to a first label vector, and
- (ii) a predetermined number of second embedding vectors corresponding to a predetermined number of vectors of second label vectors;
- values of inner products between the first label vector and the predetermined number of second label vectors being upper-order values;
- and
- learn the embedding matrix by using the first embedding vector and the predetermined number of second embedding vectors as learning data.
An information processing method comprising:
- classify a feature vector by using a classification vector, the feature vector being converted from user inputted target data, which includes at least one of a search query and a browsing activity of the user;
- converting the classified feature vector into an embedding vector in accordance with a conversion-rule;
- acquiring, as one or more labels to be provided to the target data, one or more labels acquired based on the converted embedding vector;
- acquiring (i) a first feature vector corresponding to a first label vector and
- (ii) a predetermined number of second feature vectors corresponding to a predetermined number of vectors of second label vectors, values of inner products between the first label vector and the predetermined number of second label vectors being upper-order values;
- and
- learning the classification vector by using the first feature vector and the predetermined number of second feature vectors as learning data.
A non-transitory computer-readable recording medium having stored therein a program that causes a computer to execute a process comprising:
- classify a feature vector by using a classification vector, the feature vector being converted from user inputted target data, which includes at least one of a search query and a browsing activity of the user;
- converting the classified feature vector into an embedding vector in accordance with a conversion rule;
- acquiring, as one or more labels to be provided to the target data, one or more labels acquired based on the converted embedding vector;
- acquiring (i) a first feature vector corresponding to a first label vector and
- (ii) a predetermined number of second feature vectors corresponding to a predetermined number of vectors of second label vectors, values of inner products between the first label vector and the predetermined number of second label vectors being upper-order values;
- and
- learning the classification vector by using the first feature vector and the predetermined number of second feature vectors as learning data.
Dependencies are inferred from the claim text.