Machine Learning Based Virtual Concierge for Planning Group Activities

When a group of individuals attempt to plan a group activity such as a joint trip to a common destination, the presence of conflicting constraints makes it difficult to arrive at a plan that is agreeable to all. This disclosure describes a virtual concierge that accepts as input multiple, potentially conflicting constraints from multiple individuals planning collective travel (or other group activity) and outputs optimized recommendations tailored for the individuals in the group. The virtual concierge application can leverage large language models (LLM) for language understanding and for natural user interactions. The virtual concierge can generate prompts for an LLM that has been efficiently tuned using techniques such as adapter layers, few-shot prompt tuning, etc. Machine learning (ML) can be used to generate a set of recommendations based on the preferences of different individuals in the group

Recipe Recommendations Based on Visual Input of Available Ingredients

It is difficult to choose recipes that can be prepared using ingredients available at home. Manually identifying available ingredients and performing searches for feasible recipes is cumbersome and error prone. This disclosure describes techniques that enable users to obtain recipe recommendations by capturing available ingredients visually as images and/or videos. With user permission, the visual input is analyzed using computer vision and natural language processing techniques to identify the type and quantity of available ingredients. Matching recipes are determined using a search engine and ranked based on the user’s preferences. The user can filter the list based on various criteria as well as save, label, and/or annotate specific recipes

Semi-automated User Account Profile Generation Using Existing Social Media Data

Online interactive services provide the ability for users to create profiles that specify their preferences and/or interests and showcase their photos. It is burdensome and time-consuming for users to provide manual input of the various pieces of information included in such profiles and to keep the profile updated over time. This disclosure describes techniques for automating the generation of a user profile for an online service. With user permission, a preview of the proposed user profile for a new service is generated automatically based on information about the user aggregated from the user’s other accounts. Data from the other accounts is aggregated and analyzed to determine the user’s interests and other relevant information needed for filling out the profile. The inferred interests and preferences are ranked and specific data are selected to be added to the profile, e.g., based on the likely appeal of that information for the audience on the new service. Once confirmed by the user, the information is posted to the user’s profile for the given online service