Learning drifting user interest incrementally from numerically labeled feedbacks

Most incremental approaches for learning drifting user interests assume that data instances in user feedbacks are binary labeled. A novel incremental learning approach was presented which learns drifting user interests from numerically labeled feedbacks instead of binary labeled ones. User interests were modeled as a set of probabilistic concepts. Numerical instance labels were considered as probabilities that the user likes those instances. Feedbacks were used to update user interest models incrementally based on an exponential, recency-weighted average algorithm. Experimental results on different learning tasks showed that the approach outperforms existing approaches in numerically labeled feedback environment.