Stable Confident Rating Prediction in Collaborative Filtering

Abstract

Stability of matrix factorization is estimated in terms of gradient descent estimation at each iteration which ultimately defines the stability of recommendations in collaborative filtering techniques. Also, stability is inversely proportional to total number of iterations used for estimating ratings. This gives rise to the need of the method which possesses better rating predictability within less number of iterations. The accuracy of rating prediction is found to be better when user to user trust score is estimated using similarity of individual rated items. The trust estimation is also prone to sparsity due to irregularity of ratings in large volume of data sets (big data). Based on the experimentation strategy and platform requirements, the method of trust evaluation is proposed in this positional paper. The Lyapunov stability solver functions can be used directly in obtaining solution for the trust score amongst users which can bring sufficient stability in learning stages of matrix factorization process and hence better performance in predicting the ratings of non-rated items. Here, the results obtained possess the sufficient gravity for consideration of predicted ratings which also keeps errors in prediction at lowest level for rated items. The papers are addressed from similar domain in related work section to compare proposed work in terms of novelty and performance. The results obtained are satisfactory, which are assessed in terms of mean absolute error (MAE).