National University of Defense Technology

Chengyu Song

Fei Cai

Honghui Chen

Xin Zhang

Wanyu Chen

Yitong Wang

Zhiqiang Pan

Mengru Wang

Yanxiang Ling

Taihua Shao

Dengfeng Liu

Siyuan Wang

Miao Jiang

The present disclosure provides an item recommendation method and system based on importance of item in a conversation session and a system thereof. In the present disclosure, an importance extracting module extracts an importance of each item in the conversation session, and then a long-term preference of a user is obtained in combination with the importance and the corresponding item, and then a preference of the user is obtained accurately in combination with a current interest and the long-term preference of the user, and finally item recommendation is performed according to the preference of the user. In this way, the accuracy of the item recommendation is improved.

Item recommendation method based on importance of item in conversation session and system thereof

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.

Method for predicting matching degree between resume and post, and related device

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.

Session-based recommendation method and device

The present disclosure provides an item recommendation method and system based on a user intention in a conversation session. In this method, a current conversation session is encoded by use of items of prior time sequence and the last item so as to generate an accurate conversation session representation which is then used to perform initial item prediction as a user intention. Afterwards, an intention-guided neighbor detector is used to locate an accurate neighbor conversation session. Finally, the current conversation session and the neighbor conversation session representations are combined adaptively by a gated mixed layer to generate the final item recommendation, so as to effectively improve the accuracy of item recommendation.

Item recommendation method based on user intention in a conversation session

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Hunan, China

Chengyu Song