Prediction Of User Interest By Predicting Product Text Reviews

Most item shopping websites currently provide social network services (SNS) to collect their users' opinions on items available for purchasing. This information is often used to reduce information overload and improve both the efficiency of the marketing process and user's experience by means of user-modeling and hyper-personalization of contents. Whereas a variety of recommendation systems focus almost exclusively on ranking the items, we intend to extend this basic approach by predicting the sets of words that users would use should they express their opinions and interests on items not yet reviewed. To this end, we pay careful attention to the internal consistency of our model by relying on well-known facts of linguistic analysis, collaborative filtering techniques and matrix factorization methods. Still at an early stage of development, we discuss some encouraging results and open challenges of this new approach.