Abstract: A conversational recommendation method, a method of training a conversational recommendation model, an electronic device, and a storage medium are provided, which are related to a technical field of data processing, in particular to technical fields of voice interaction, deep learning, artificial intelligence and the like. The conversational recommendation method includes: acquiring a historical conversation information; determining a target conversation object to be generated, from a conversation target graph based on the historical conversation information, the conversation target graph includes an object node, the object node is configured to represent a conversation object, and the target conversation object is determined based on the object node; and generating a target conversation information for recommendation based on the target conversation object.

Abstract: The present disclosure provides a method of training an information generation model, a method of generating an information, an electronic device, and a storage medium. A specific implementation solution of the method of training the information generation model includes: splitting a description information for a target object in an information pair into at least one description word, so as to obtain a description word sequence, wherein the information pair further includes a first recommendation information; inputting the description word sequence into a dialog generation model to obtain a probability vector sequence for the target object, wherein each probability vector in the probability vector sequence includes probability values for a plurality of predetermined words; and training the dialog generation model according to the probability vector sequence and the first recommendation information, so as to obtain the information generation model.

Abstract: The present disclosure provides a sentence recommendation method and apparatus based on associated points of interest. The method includes: obtaining an input sentence from a user; extracting a keyword in the input sentence, and searching for a current point of interest matching the keyword in a preset point of interest association network determining a plurality of associated points of interest matching the current point of interest according to the preset point of interest association network, and filtering out an extension point of interest from the plurality of associated points of interest according to a preset filter strategy; and retrieving a first reply sentence and a second reply sentence from a preset corpus according to the current point of interest and the extension point of interest, and providing them to the user.

Abstract: The present disclosure provides a dialog method and system, an electronic device and a storage medium, and relates to the field of artificial intelligence (AI) technologies such as deep learning and natural language processing. A specific implementation scheme involves: rewriting a corresponding dialog state based on received dialog information of a user; determining to-be-used dialog action information based on the dialog information of the user and the dialog state; and generating a reply statement based on the dialog information of the user and the dialog action information. According to the present disclosure, the to-be-used dialog action information can be determined based on the dialog information of the user and the dialog state; and then the reply statement is generated based on the dialog action information, thereby providing an efficient dialog scheme.

Abstract: A method for generating a conversation, an electronic device and a storage medium, which relate to the field of artificial intelligence, are disclosed. The method may include: acquiring conversation content to be replied; determining an event node matched with the conversation content from an event graph, the event graph being a pre-constructed directed graph and including event nodes corresponding to different events respectively, and sides between the event nodes indicating logical relationships between the different events; determining an event node for guiding reply generation from the event graph according to the matched event node and the connection mode among the event nodes; and generating conversation reply content according to the event node for guiding reply generation. With the technical solution, dialog coherent, informative, and engaging multi-turn conversation may be generated.

Abstract: A first expression corresponding to an input statement is obtained from a conversational graph. The conversational graph includes expressions having association relationships therebetween and conversation target clusters having association relationships therebetween. Each conversation target cluster includes at least two expressions. A second expression associated with the first expression is obtained from the conversational graph based on the association relationships of expressions and the association relationships of conversational target clusters in the conversational graph. A reply statement is generated based on the second expression and the input statement.

Abstract: A human-machine interaction method is related to the field of artificial intelligence technologies. The method includes: obtaining a conversation sentence input by a user; obtaining a query sentence matching the conversation sentence; obtaining a plurality of associated query sentences corresponding to the query sentence based on a preset query word graph; processing the conversation sentence and the plurality of associated query sentences through a preset algorithm to select a target query sentence from the plurality of associated query sentences; and processing the target query sentence based on a preset response generation model to generate a response sentence for the user.

Abstract: The present disclosure provides a sentence recommendation method and apparatus based on associated points of interest. The method includes: obtaining an input sentence from a user; extracting a keyword in the input sentence, and searching for a current point of interest matching the keyword in a preset point of interest association network determining a plurality of associated points of interest matching the current point of interest according to the preset point of interest association network, and filtering out an extension point of interest from the plurality of associated points of interest according to a preset filter strategy; and retrieving a first reply sentence and a second reply sentence from a preset corpus according to the current point of interest and the extension point of interest, and providing them to the user.