User modeling

About: User modeling is a research topic. Over the lifetime, 10701 publications have been published within this topic receiving 278012 citations.

Search interface for mobile devices

Abstract: The subject disclosure pertains to systems and methods for providing a user interface for mobile devices. The user interface can include a search function that utilizes user input to search multiple software applications and data sources. Content can be retrieved and presented to a user without requiring the user to navigate to the underlying application or sources supplying the content. The applications or content sources can be local to the mobile device or remotely located, such as an Internet site. One or more types of data can be retrieved and presented to a user based upon the user input. Search results can be displayed and dynamically updated as additional user input is obtained.

User Modeling, Adaptation, and Personalization

Abstract: The social web is having a clear impact in our field of user modeling and adaptation. ‘Links’ and ‘Likes’ as well as ‘Followers’ and ‘Friends’ are part of a large source of data that is generated by users themselves, often for different purposes, and that provides an unprecedented potential for systems to understand their users and to adapt based on that understanding. As we can see from researchers and projects in a number of relevant fields, data on various manifestations of what users do socially on the web brings new opportunities. Exciting ideas are generated and first explorations show promising results. In this talk we take a look back at recent proposals and studies that consider the social web. We determine interesting patterns and we aim to understand the impact on methods and techniques for user modeling and adaptation. At the same time, the social web brings even more challenges. We look forward by identifying challenges that can drive our research. From technical challenges to explore the different social web sources to social challenges to understand how users behave when this potential is unlocked.

Real-time Personalization using Embeddings for Search Ranking at Airbnb

Abstract: Search Ranking and Recommendations are fundamental problems of crucial interest to major Internet companies, including web search engines, content publishing websites and marketplaces. However, despite sharing some common characteristics a one-size-fits-all solution does not exist in this space. Given a large difference in content that needs to be ranked, personalized and recommended, each marketplace has a somewhat unique challenge. Correspondingly, at Airbnb, a short-term rental marketplace, search and recommendation problems are quite unique, being a two-sided marketplace in which one needs to optimize for host and guest preferences, in a world where a user rarely consumes the same item twice and one listing can accept only one guest for a certain set of dates. In this paper we describe Listing and User Embedding techniques we developed and deployed for purposes of Real-time Personalization in Search Ranking and Similar Listing Recommendations, two channels that drive 99% of conversions. The embedding models were specifically tailored for Airbnb marketplace, and are able to capture guest's short-term and long-term interests, delivering effective home listing recommendations. We conducted rigorous offline testing of the embedding models, followed by successful online tests before fully deploying them into production.

A personalized system for conversational recommendations

Abstract: Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system - the ADAPTIVE PLACE ADVISOR - that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system.

Computer-aided group-learning methods and systems

Abstract: Providing methods and systems for a computer-aided group-learning environment, where a number of users can interact and work on a subject together. The system and method can monitor and analyze users' inputs. The analysis process can identify a user's performance on the subject, and can understand some of the user's traits, such as confidence level and learning attitude. The system can include an interaction controller, which sets a duration of time for the users to communicate in a dialogue environment. Working on the subject in a group and working alone can be intertwined. For example, the users first work on the materials generated individually, and then solve the problem together in a dialogue environment. During the dialogue session, the interaction controller can provide hints to the users. The system can also include a user registry, which restricts the users who can use the embodiment to work on the subject. The registry can receive potential user's characteristics to determine whether such user may be allowed to join the existing users to work on the subject. The registry can also access a summarized profile of the existing users to help the potential user make joining decisions. The system can also include a notepad for a user to take notes. The interaction controller can also guide the user to take notes.