Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The present disclosure provides an item recommendation method and system based on a user intention in a session. In this method, a current session is encoded by use of items of prior time sequence and the last item so as to generate an accurate 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 session. Finally, the current session and the neighbor 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.

Abstract: The present disclosure provides an item recommendation method based on importance of item in a session and a system thereof. In the present disclosure, an importance extracting module extracts an importance of each item in the 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.