基于二分网络的长期推荐

Long-term recommendation based on bipartite network

目前在基于二分网络的推荐算法研究中,关注更多的是推荐的短期性能,而在现实生活中,对每一个用户的推荐是一个长期的过程,在线网络会随着时间的推移而发展,并且用户在购物时往往有求新的消费心理,因此长期推荐的多样性也需要更多的关注。针对这些问题,将短期推荐中表现良好的经典算法应用到长期推荐中,发现长期的推荐多样性和准确性逐渐变差;为了改善长期推荐的表现,设计了一个融合时间因子的推荐算法,并将其应用到长期推荐中;实验结果表明,提出的算法在不损失推荐准确性的前提下,显著提高了长期推荐的多样性。

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.