User modeling

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

Computer-human interface which provides for user customization of object behavior

Abstract: A computer-user interface facilitates interaction between the user and the computer in a manner which enables the computer to assist the user in accomplishing desired goals. The computer works with the user to automatically exhibit desired behavior in response to triggering events designated by the user. Any executable function can be designated by the user as the object of a task. The initiation of this task can be carried out in response to any programmatically detectable event, or trigger, which is also designated by the user. The existence and implementation of the behavior is unknown to the files and other objects with which it is associated. With the flexibility offered by this arrangement, the user can customize the operation of a computer to suit his or her particular needs.

Collecting user access patterns for building user profiles and collaborative filtering

Abstract: The paper proposes a new learning mechanism to extract user preferences transparently for a World Wide Web recommender system. The general idea is that we use the entropy of the page being accessed to determine its interestingness based on its occurrence probability following a sequence of pages accessed by the user. The probability distribution of the pages is obtained by collecting the access patterns of users navigating on the Web. A finite context-model is used to represent the usage information. Based on our proposed model, we have developed an autonomous agent, named ProfBuilder, that works as an online recommender system for a Web site. ProfBuilder uses the usage information as a base for content-based and collaborative filtering.

Techniques for sharing content information with members of a virtual user group in a network environment without compromising user privacy

Abstract: Techniques for sharing content information between members of a virtual user group without compromising the privacy of the members. A user can identify content information to be shared with other members of a virtual user group using a user computer system. The content information is then communicated to the other members of the virtual user group and can be accessed by members of the virtual user group in such a manner that the privacy of the user and of the other members of the virtual user group is not compromised. The present invention preserves user privacy by controlling and minimizing the amount of user-related information available/accessible to server systems hosting the virtual user groups.

Enabling searching of user ratings and reviews using user profile location, and social networks

Abstract: A system and method are directed towards a free-form search query of user reviews using user profile, location information, and/or social networks, to obtain a result having an associated universal aggregated rating. The user may enter in free-form a search query that may then be transparently modified using the user's profile, social network, and/or current physical location. The search results may then be presented to the user along with aggregated weighted ratings. The user may also enter products and/or services into a data store, including comments, and a universal rating. In one embodiment, the user may provide a tag to another reviewer's comments that may be useable to aggregate ratings. In one embodiment, the user's profile, location, and/or social networking information may be used to further annotate the user's inputs.

ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation.

Abstract: A user can be represented as what he/she does along the history. A common way to deal with the user modeling problem is to manually extract all kinds of aggregated features over the heterogeneous behaviors, which may fail to fully represent the data itself due to limited human instinct. Recent works usually use RNN-based methods to give an overall embedding of a behavior sequence, which then could be exploited by the downstream applications. However, this can only preserve very limited information, or aggregated memories of a person. When a downstream application requires to facilitate the modeled user features, it may lose the integrity of the specific highly correlated behavior of the user, and introduce noises derived from unrelated behaviors. This paper proposes an attention based user behavior modeling framework called ATRank, which we mainly use for recommendation tasks. Heterogeneous user behaviors are considered in our model that we project all types of behaviors into multiple latent semantic spaces, where influence can be made among the behaviors via self-attention. Downstream applications then can use the user behavior vectors via vanilla attention. Experiments show that ATRank can achieve better performance and faster training process. We further explore ATRank to use one unified model to predict different types of user behaviors at the same time, showing a comparable performance with the highly optimized individual models.