Abstract: A method and an apparatus for few-shot relation classification and filtering, and a device are provided. The method includes: constructing a coarse-grained filter for filtering an unlabeled corpus to obtain candidate instances with a same entity as a seed instance and with similar semantics to the seed instance; constructing a fine-grained filter for filtering the candidate instances to obtain a candidate instance with a same relation concept as the seed instance; defining the candidate instance as a positive instance set, and defining candidate instances with different relation concepts from the seed instance as a negative sample set; constructing a false positive instance correction module for adjusting and controlling a proportion of the negative sample set used by a classifier during training; training the classifier based on a small number of obtained labeled instances belonging to a newly emerging relation and the adjusted positive instance set and negative sample set.

Abstract: A method for predicting matching degree between a resume and a post, and a related device are provided in this disclosure. In the method for predicting the matching degree between the resume and the post, and the related device according to this disclosure, firstly the semi-structured keys and values in post information and resume information and their source are obtained. Then, the matching degree between the resume information and the post information is predicted by a prediction model including a cascaded pre-trained language model, a Transformer encoder and a single label classification model, based on the keys and values of a respective post information and resume information attribute, and corresponding source representations. Thus, by comprehensively searching internal interaction and external interaction of semi-structured multivariate attributes in person-post matching, the matching result is more accurate.

Abstract: A method for predicting matching degree between a resume and a post, and a related device are provided in this disclosure. In the method for predicting the matching degree between the resume and the post, and the related device according to this disclosure, firstly the semi-structured keys and values in post information and resume information and their source are obtained. Then, the matching degree between the resume information and the post information is predicted by a prediction model including a cascaded pre-trained language model, a Transformer encoder and a single label classification model, based on the keys and values of a respective post information and resume information attribute, and corresponding source representations. Thus, by comprehensively searching internal interaction and external interaction of semi-structured multivariate attributes in person-post matching, the matching result is more accurate.

Abstract: A session-based recommendation method and device according to one or more embodiments of this disclosure are provided, which use a pre-trained recommendation model to perform item recommend. The method includes following contents: a directed session graph is constructed according to a session to be predicted; the directed session graph is then input into a gated graph neural network which outputs the item embedding vector; a user's dynamic preference is determined according to a user's current preference and a first long-term preference, the current preference is an item embedding vector of a last item in the session and the first long-term preference is determined according to the item embedding vector and an importance score of the item; a prediction score of a respective item is determined according to the dynamic preference and the item embedding vector; and a recommended item is output according to the prediction score of the respective item.

Abstract: A session-based recommendation method and device according to one or more embodiments of this disclosure are provided, which use a pre-trained recommendation model to perform item recommend. The method includes following contents: a directed session graph is constructed according to a session to be predicted; the directed session graph is then input into a gated graph neural network which outputs the item embedding vector; a user's dynamic preference is determined according to a user's current preference and a first long-term preference, the current preference is an item embedding vector of a last item in the session and the first long-term preference is determined according to the item embedding vector and an importance score of the item; a prediction score of a respective item is determined according to the dynamic preference and the item embedding vector; and a recommended item is output according to the prediction score of the respective item.