Nachiketa Sahoo

Incremental hierarchical clustering of text documents

Incremental hierarchical text document clustering algorithms are important in organizing documents generated from streaming on-line sources, such as, Newswire and Blogs. However, this is a relatively unexplored area in the text document clustering literature. Popular incremental hierarchical clustering algorithms, namely Cobweb and Classit, have not been widely used with text document data. We discuss why, in the current form, these algorithms are not suitable for text clustering and propose an alternative formulation that includes changes to the underlying distributional assumption of the algorithm in order to conform with the data. Both the original Classit algorithm and our proposed algorithm are evaluated using Reuters newswire articles and Ohsumed dataset.

Research Note---The Halo Effect in Multicomponent Ratings and Its Implications for Recommender Systems: The Case of Yahoo! Movies

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 instance-based 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.

How to Attract and Retain Readers in Enterprise Blogging

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 sentiment and quality of a post on attracting readers from retaining them. We find that the textual characteristics that appeal to the sentiment of the reader affect both reader attraction and retention. However, textual characteristics that reflect only the quality of the posts affect only reader retention. We identify a variety-seeking 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.

The Impact of Online Product Reviews on Product Returns

Although many researchers in Information Systems and Marketing have studied the effect of product reviews on sales, few have looked at their effect on product returns. We hypothesize that, by affecting the quality of purchase decisions, product reviews influence the probability of the eventual return of the purchased products. We elaborate this hypothesis by developing an analytical model that shows how changes in the precision of product quality and fit information affect the return probabilities of risk- averse, but rational, consumers. We empirically validate the predictions of our theory using a transaction level dataset from a multi-channel, multi-brand specialty retailer operating in North America. Harnessing data on multiple purchases and returns of the same products, but with varying sets of product reviews over a period of two years, we find that the availability of higher volumes of reviews, as well as review bodies that contain a higher percentage of reviews that are designated as ‘helpful’ by consumers, lead to lower incidence of product returns, after controlling for customer, product and other context-related factors. These results are consistent with the predictions of our theoretical model, and suggest that online reviews indeed help consumers make better purchase decisions leading to lower product returns.