We investigate the dynamics of blog reading behavior of employees in an enterprise blogosphere. A dynamic model is developed and calibrated using longitudinal data from a Fortune 1,000 IT services firm. Our modeling framework allows us to segregate the impact of textual characteristics (<i>sentiment</i> and <i>quality</i>) of a post on attracting readers from retaining them. We find that the textual characteristics that appeal to the <i>sentiment</i> of the reader affect both reader attraction and retention. However, textual characteristics that reflect only the <i>quality</i> of the posts affect only reader retention. We identify a <i>variety-seeking</i> behavior of blog readers where they dynamically switch from reading on one set of topics to another. The modeling framework and findings of this study highlight opportunities for the firm to influence blog-reading behavior of its employees to align it with its goals. Overall, this study contributes to improved understanding of reading behavior of individuals in communities formed around user generated content.
Collaborative filtering algorithms learn from the ratings of a group of users on a set of items to find personalized recommendations for each user. Traditionally they have been designed to work with one-dimensional ratings. With interest growing in recommendations based on multiple aspects of items, we present an algorithm for using multicomponent rating data. The presented mixture model-based algorithm uses the component rating dependency structure discovered by a structure learning algorithm. The structure is supported by the psychometric literature on the halo effect. This algorithm is compared with a set of model-based and instancebased algorithms for single-component ratings and their variations for multicomponent ratings. We evaluate the algorithms using data from Yahoo! Movies. Use of multiple components leads to significant improvements in recommendations. However, we find that the choice of algorithm depends on the sparsity of the training data. It also depends on whether the task of the algorithm is to accurately predict ratings or to retrieve relevant items. In our experiments a model-based multicomponent rating algorithm is able to better retrieve items when training data are sparse. However, if the training data are not sparse, or if we are trying to predict the rating values accurately, then the instance-based multicomponent rating collaborative filtering algorithms perform better. Beyond generating recommendations we show that the proposed model can fill in missing rating components. Theories in psychometric literature and the empirical evidence suggest that rating specific aspects of a subject is difficult. Hence, filling in the missing component values leads to the possibility of a rater support system to facilitate gathering of multicomponent ratings.