Abstract/Summary

Recommender systems are of great significance in predicting the potential interesting items based on the target user’s historical selections. However, the recommendation list for a specific user has been found changing vastly when the system changes, due to the unstable quantification of item similarities, which is defined as the recommendation stability problem. To improve the similarity stability and recommendation stability is crucial for the user experience enhancement and the better understanding of user interests. While the stability as well as accuracy of recommendation could be guaranteed by recommending only popular items, studies have been addressing the necessity of diversity which requires the system to recommend unpopular items. By ranking the similarities in terms of stability and considering only the most stable ones, we present a top-n-stability method based on the Heat Conduction algorithm (denoted as TNS-HC henceforth) for solving the stability-accuracy-diversity dilemma. Experiments on four benchmark data sets indicate that the TNS-HC algorithm could significantly improve the recommendation stability and accuracy simultaneously and still retain the high-diversity nature of the Heat Conduction algorithm. Furthermore, we compare the performance of the TNS-HC algorithm with a number of benchmark recommendation algorithms. The result suggests that the TNS-HC algorithm is more efficient in solving the stability-accuracy-diversity triple dilemma of recommender systems.