Abstract: Nowadays, most studies about recommender systems based on the bipartite network focus on the short-term performance of algorithms. However, in real life, recommendation for each user are a long-term process, and online networks evolve over time. Meanwhile, users tend to select novel goods when shopping. Therefore, it is necessary to pay more attention to the diversity of long-term recommendations. Aiming at the problem, the classical algorithm with good performance in short-term recommendations is applied to long-term recommendations and the diversity and accuracy of long-term recommendations are both gradually decreased. To improve the performance of long-term recommendations, a recommendation algorithm that incorporates the time factor is designed, and applied to the long-term recommendation. Experimental results show that the proposed algorithm significantly improves the long-term recommendation diversity without losing recommendation accuracy.

Key words: recommender system, bipartite network, long-term recommendation, diffusion-based algorithm, time information