%0 Conference Paper %1 citeulike:342639 %A Liu, Hugo %A Maes, Pattie %B Beyond Personalisation %D 2004 %K collaborative filtering interest network profile social %T InterestMap: Harvesting Social Network Profiles for Recommendations %U http://web.media.mit.edu/~hugo/publications/papers/BP2005-hugo-interestmap.pdf %X While most recommender systems continue to gather detailed models of their “users” within their particular application domain, they are, for the most part, oblivious to the larger context of the lives of their users outside of the application. What are they pas-sionate about as individuals, and how do they identify themselves culturally? As recommender systems become more central to people’s lives, we must start modeling the person, rather than the user. In this paper, we explore how we can build models of people outside of narrow application domains, by capturing the traces they leave on the Web, and inferring their everyday interests from this. In particular, for this work, we harvested 100,000 social network profiles, in which people describe themselves using a rich vocabulary of their passions and interests. By automatically analyzing patterns of correlation between various interests and cultural identities (e.g. “Raver,” “Dog Lover,” “Intellectual”), we built InterestMap, a network-style view of the space of intercon-necting interests and identities. Through evaluation and discus-sion, we suggest that recommendations made in this network space are not only accurate, but also highly visually intelligible – each lone interest contextualized by the larger cultural milieu of the network in which it rests.

@inproceedings{citeulike:342639, abstract = {While most recommender systems continue to gather detailed models of their “users” within their particular application domain, they are, for the most part, oblivious to the larger context of the lives of their users outside of the application. What are they pas-sionate about as individuals, and how do they identify themselves culturally? As recommender systems become more central to people’s lives, we must start modeling the person, rather than the user. In this paper, we explore how we can build models of people outside of narrow application domains, by capturing the traces they leave on the Web, and inferring their everyday interests from this. In particular, for this work, we harvested 100,000 social network profiles, in which people describe themselves using a rich vocabulary of their passions and interests. By automatically analyzing patterns of correlation between various interests and cultural identities (e.g. “Raver,” “Dog Lover,” “Intellectual”), we built InterestMap, a network-style view of the space of intercon-necting interests and identities. Through evaluation and discus-sion, we suggest that recommendations made in this network space are not only accurate, but also highly visually intelligible – each lone interest contextualized by the larger cultural milieu of the network in which it rests.}, added-at = {2007-08-15T12:02:44.000+0200}, author = {Liu, Hugo and Maes, Pattie}, biburl = {https://www.bibsonomy.org/bibtex/21eaea7865cb52ad61639eb53cadf32af/wnpxrz}, booktitle = {Beyond Personalisation}, citeulike-article-id = {342639}, comment = {Methods: Ontology, Recommender Systems style Profile. Method for learning semantic relatedness weights: Pointwise Mutual Information (Church et al, 91) [instead of Latent Semantic Analysis] Domain: Friendster, Orkut, MySpace Task: identify interest classification, interest recommendation, interest-based matchmaking}, interhash = {9204dbae9c671327f3211c5bd395e851}, intrahash = {1eaea7865cb52ad61639eb53cadf32af}, keywords = {collaborative filtering interest network profile social}, month = {December}, priority = {0}, timestamp = {2007-08-15T12:02:44.000+0200}, title = {InterestMap: Harvesting Social Network Profiles for Recommendations}, url = {http://web.media.mit.edu/~hugo/publications/papers/BP2005-hugo-interestmap.pdf}, year = 2004 }