The effectiveness of existing top-N recommendation methods decreases as the sparsity of the datasets increases. To alleviate this problem, we present an item-based method for generating top-N recommendations that learns the item-item similarity matrix as the product of two low dimensional latent factor matrices. These matrices are learned using a structural equation modeling approach, wherein the value being estimated is not used for its own estimation. A comprehensive set of experiments on multiple datasets at three different sparsity levels indicate that the proposed methods can handle sparse datasets effectively and outperforms other state-of-the-art top-N recommendation methods. The experimental results also show that the relative performance gains compared to competing methods increase as the data gets sparser.

Demographic information plays an important role in gaining valuable insights about a web-site's user-base and is used extensively to target online advertisements and promotions. This paper investigates machine-learning approaches for predicting the demographic attributes of web-sites using information derived from their content and their hyper linked structure and not relying on any information directly or indirectly obtained from the web-site's users. Such methods are important because users are becoming increasingly more concerned about sharing their personal and behavioral information on the Internet. Regression-based approaches are developed and studied for predicting demographic attributes that utilize different content-derived features, different ways of building the prediction models, and different ways of aggregating web-page level predictions that take into account the web's hyper linked structure. In addition, a matrix-approximation based approach is developed for coupling the predictions of individual regression models into a model designed to predict the probability mass function of the attribute. Extensive experiments show that these methods are able to achieve an RMSE of 8-10% and provide insights on how to best train and apply such models.