Detecting User Communities Based on Latent and Dynamic Interest on a News Portal
Marián Hönsch · 2011
This paper describes our work on identifying communities of individuals based on their interests while browsing the web. A user can belong to several communities at a time, where each community represents parts of his interests. We assume that recommendations coming from such communities are more accurate than from communities based on a whole user profile. We describe how to record and identify particular interests for each user. Interests evolve from analysis of the resources that the user has viewed in the past and are defined as cluster of keywords. Based on the time period we model short term and long term interests. The novel approach is to create virtual communities based on these interests, both short and long. To evaluate our approach we built articles recommender for a news portal. As recommender systems are tailored to the specific domain, we also adapted our approach slightly to better fit the news portal domain, which is highly dynamic and with frequent changes. We consider these time-dependent changes by weighting the influence of volatile communities on recommendations.