Abstract: Aspect pre-selection techniques using machine learning are described. In one example, an artificial assistant system is configured to implement a chat bot. A user then engages in a first natural-language conversation. As part of this first natural-language conversation, a communication is generated by the chat bot to prompt the user to specify an aspect of a category that is a subject of a first natural-language conversation and user data is received in response. Data that describes this first natural-language conversation is used to train a model using machine learning. Data is then received by the chat bot as part of a second natural-language conversation. This data, from the second natural-language conversation, is processed using the model as part of machine learning to generate the second search query to include the aspect of the category automatically and without user intervention.

Abstract: Aspect pre-selection techniques using machine learning are described. In one example, an artificial assistant system is configured to implement a chat bot. A user then engages in a first natural-language conversation. As part of this first natural-language conversation, a communication is generated by the chat bot to prompt the user to specify an aspect of a category that is a subject of a first natural-language conversation and user data is received in response. Data that describes this first natural-language conversation is used to train a model using machine learning. Data, is then be received by the chat bot as part of a second natural-language conversation. This data, from the second natural-language conversation, is processed using the model as part of machine learning to generate the second search query to include the aspect of the category automatically and without user intervention.

Abstract: A guided search system for suggesting and arranging filter criteria within a user interface for presentation to a user to help guide the user’s search for listings is disclosed. The system builds one or more filter criteria frequency data structures indicative of the number of times each filter criterion has been used to filter search results and how often different filter criteria are used together. The system uses the frequency data structures to predict which filter criteria a user will likely employ to narrow their search given the filter criteria the user has already used. The system provides techniques for arranging or rearranging filter criteria within a user interface, by moving, placing, or ordering suggested filter criteria within the user interface, where a user is likely to be able to recognize and interact with the placed filter criteria, based on the determined amounts of use.

Abstract: Artificial assistant system notification techniques are described that overcome the challenges of conventional search techniques. In one example, a user profile is generated to describe aspects of products or services learned through natural language conversations between a user and an artificial assistant system. These aspects may include price as well as non-price aspects such as color, texture, material, and so forth. To learn the aspects, the artificial assistant system may leverage spoken utterances and text initiated by the user as well as learn the aspects from digital images output as part of the conversation. Once generated, the user profile is then usable by the artificial assistant system to assist in subsequent searches.

Abstract: Artificial assistant system notification techniques are described that overcome the challenges of conventional search techniques. In one example, a user profile is generated to describe aspects of products or services learned through natural language conversations between a user and an artificial assistant system. These aspects may include price as well as non-price aspects such as color, texture, material, and so forth. To learn the aspects, the artificial assistant system may leverage spoken utterances and text initiated by the user as well as learn the aspects from digital images output as part of the conversation. Once generated, the user profile is then usable by the artificial assistant system to assist in subsequent searches.

Abstract: A guided search system for suggesting and arranging filter criteria within a user interface for presentation to a user to help guide the user's search for listings is disclosed. The system builds one or more filter criteria frequency data structures indicative of the number of times each filter criterion has been used to filter search results and how often different filter criteria are used together. The system uses the frequency data structures to predict which filter criteria a user will likely employ to narrow their search given the filter criteria the user has already used. The system provides techniques for arranging or rearranging filter criteria within a user interface, by moving, placing, or ordering suggested filter criteria within the user interface, where a user is likely to be able to recognize and interact with the placed filter criteria, based on the determined amounts of use.

Abstract: A guided search system for suggesting and arranging filter criteria within a user interface for presentation to a user to help guide the user's search for listings is disclosed. The system builds one or more filter criteria frequency data structures indicative of the number of times each filter criterion has been used to filter search results and how often different filter criteria are used together. The system uses the frequency data structures to predict which filter criteria a user will likely employ to narrow their search given the filter criteria the user has already used. The system provides techniques for arranging or rearranging filter criteria within a user interface, by moving, placing, or ordering suggested filter criteria within the user interface, where a user is likely to be able to recognize and interact with the placed filter criteria, based on the determined amounts of use.

Abstract: Aspect pre-selection techniques using machine learning are described. In one example, an artificial assistant system is configured to implement a chat bot. A user then engages in a first natural-language conversation. As part of this first natural-language conversation, a communication is generated by the chat bot to prompt the user to specify an aspect of a category that is a subject of a first natural-language conversation and user data is received in response. Data that describes this first natural-language conversation is used to train a model using machine learning. Data, is then be received by the chat bot as part of a second natural-language conversation. This data, from the second natural-language conversation, is processed using the model as part of machine learning to generate the second search query to include the aspect of the category automatically and without user intervention.

Abstract: Aspect pre-selection techniques using machine learning are described. In one example, an artificial assistant system is configured to implement a chat bot. A user then engages in a first natural-language conversation. As part of this first natural-language conversation, a communication is generated by the chat bot to prompt the user to specify an aspect of a category that is a subject of a first natural-language conversation and user data is received in response. Data that describes this first natural-language conversation is used to train a model using machine learning. Data, is then be received by the chat bot as part of a second natural-language conversation. This data, from the second natural-language conversation, is processed using the model as part of machine learning to generate the second search query to include the aspect of the category automatically and without user intervention.

Abstract: Artificial assistant system notification techniques are described that overcome the challenges of conventional search techniques. In one example, a user profile is generated to describe aspects of products or services learned through natural language conversations between a user and an artificial assistant system. These aspects may include price as well as non-price aspects such as color, texture, material, and so forth. To learn the aspects, the artificial assistant system may leverage spoken utterances and text initiated by the user as well as learn the aspects from digital images output as part of the conversation. Once generated, the user profile is then usable by the artificial assistant system to assist in subsequent searches.

Abstract: Aspect pre-selection techniques using machine learning are described. In one example, an artificial assistant system is configured to implement a chat bot. A user then engages in a first natural-language conversation. As part of this first natural-language conversation, a communication is generated by the chat bot to prompt the user to specify an aspect of a category that is a subject of a first natural-language conversation and user data is received in response. Data that describes this first natural-language conversation is used to train a model using machine learning. Data, is then be received by the chat bot as part of a second natural-language conversation. This data, from the second natural-language conversation, is processed using the model as part of machine learning to generate the second search query to include the aspect of the category automatically and without user intervention.