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User interest modeling and Personalization

1. Unsupervised Modeling of Users’ Interests from their Facebook Profiles and Activities

Mentors: Oliver Brdiczka and Michael Roberts (Palo Alto Research Center)

Abstract: 

User interest profiles have become essential for personalizing information streams and services, and user interfaces
and experiences. In today’s world, social networks such as Facebook or Twitter provide users with a powerful platform for interest expression and can, thus, act as a rich content source for automated user interest modeling. This, however, poses significant challenges because the user generated content on them consists of free unstructured text. In addition, users may not explicitly post or tweet about everything that interests them. Moreover, their interests evolve over time. In this paper, we
propose a novel unsupervised algorithm and system that addresses these challenges. It models a broad range of an individual user’s explicit and implicit interests from her social network profile and activities without any user input. We perform extensive evaluation of our system, and algorithm, with a dataset consisting of 488 active Facebook users’ profiles and demonstrate that it can accurately estimate a user’s interests in practice.

Related publications:

a. Preeti BhargavaOliver Brdiczka, Michael Roberts, Unsupervised Modeling of Users’ Interests from their Facebook Profiles and ActivitiesProceedings of the 20th ACM conference on Intelligent User Interfaces (IUI 2015) [PDF]

2. Mining users' online communication for improved interaction with context-aware systems

Mentors: Oliver Brdiczka and Michael Roberts (Palo Alto Research Center)

Abstract: 

With the advent of the internet, online communication media and social networks have become increasingly popular among users for interaction and communication. Integrating these online communications with other sources of a user’s context can help improve his interaction with context-aware systems as it enables the systems to provide highly personalized content to both individual and groups of users. To this end, a user’s communication context (such as the people he communicates with often, and the topics he discusses frequently) becomes an important aspect of his context model and new frameworks and methodologies are required for extracting and representing it. In this paper, we present a hybrid framework derived from traditional graph based and object oriented models that employs various Natural Language Processing techniques for extracting and representing users’ communication context from their aggregated online communications. We also evaluate the framework using the email communication log of a user.

Related publications:

a. Preeti BhargavaOliver Brdiczka, Michael Roberts, Mining users' online communication for improved interaction with context-aware systemsProceedings of the IUI 2015 Workshop on Interacting with Smart Objects [PDF]
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