Similarity matrix enhanced collaborative filtering for e-government recommendation

Abstract

Personalized e-government recommendation aims to predict the user’s next action in a near future based on his or her historical behavior data. Similarity measures and distances are widely used for powering personalized recommendations. The classical similarity-based models in the recommender systems are known as K-Nearest Neighbor Collaborative Filtering (KNN-based CF). Most prior work adopts a new similarity formula to obtain the k-nearest neighbors for the user and/or item. This is a good try but falls short of solving the problem of expensive computation and introducing the order of users’ interaction sequences. In this paper, we propose SACF, a similarity matrix enhanced the e-government recommendation model that takes advantage of pairwise learning to introduce the order of uses’ interaction sequences and quickly obtain the similarity matrix using matrix factorization. In other words, the similarity matrix can be presented as the production of two low-rank feature matrices. Therefore, it is possible to effectively estimate the similarity between two users even with few co-rated items. Experimental results on a real-world e-government dataset show that the proposed approach improves the performance significantly compared with other counterparts.