Unsupervised Modeling of Users' Interests from their Facebook Profiles and Activities

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.

[1]  Jennifer Golbeck,et al.  Predicting personality with social media , 2011, CHI Extended Abstracts.

[2]  Krishna P. Gummadi,et al.  On the evolution of user interaction in Facebook , 2009, WOSN '09.

[3]  Ana-Maria Popescu,et al.  Democrats, republicans and starbucks afficionados: user classification in twitter , 2011, KDD.

[4]  Qi Gao,et al.  Analyzing user modeling on twitter for personalized news recommendations , 2011, UMAP'11.

[5]  Andrea Leganza.  Approved for External Publication , 2005 .

[6]  James Bennett,et al.  The Netflix Prize , 2007 .

[7]  Ryen W. White,et al.  Predicting user interests from contextual information , 2009, SIGIR.

[8]  Eelco Herder,et al.  Extraction of Professional Interests from Social Web Profiles , 2011 .

[9]  T. Graepel,et al.  Private traits and attributes are predictable from digital records of human behavior , 2013, Proceedings of the National Academy of Sciences.

[10]  Philip K. Chan,et al.  Learning implicit user interest hierarchy for context in personalization , 2008, IUI '03.

[11]  Barry Smyth,et al.  CatStream: categorising tweets for user profiling and stream filtering , 2013, IUI '13.

[12]  Jiahui Liu,et al.  Personalized news recommendation based on click behavior , 2010, IUI '10.

[13]  Jonathan Weese,et al.  UMBC_EBIQUITY-CORE: Semantic Textual Similarity Systems , 2013, *SEMEVAL.

[14]  Yi Zeng,et al.  User Interests Modeling Based on Multi-source Personal Information Fusion and Semantic Reasoning , 2011, AMT.

[15]  Krishna P. Gummadi,et al.  You are who you know: inferring user profiles in online social networks , 2010, WSDM '10.

[16]  Barry Smyth,et al.  On the real-time web as a source of recommendation knowledge , 2010, RecSys '10.

[17]  Din J. Wasem,et al.  Mining of Massive Datasets , 2014 .

[18]  Feng Qiu,et al.  Automatic identification of user interest for personalized search , 2006, WWW '06.

[19]  John Riedl,et al.  An algorithmic framework for performing collaborative filtering , 1999, SIGIR '99.

[20]  Gareth Jones,et al.  Building user interest profiles from wikipedia clusters , 2011 .

[21]  Sophie Ahrens,et al.  Recommender Systems , 2012 .

[22]  Rajat Raina,et al.  Understanding the Interaction between Interests, Conversations and Friendships in Facebook , 2012, ArXiv.

[23]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[24]  Max Kaufmann.  Syntactic Normalization of Twitter Messages , 2010 .

[25]  Pushmeet Kohli,et al.  Personality and patterns of Facebook usage , 2012, WebSci '12.