一种数据递增式的混合推荐方法

推荐系统由于较大的训练数据量和推荐算法较高的复杂度，其推荐的更新周期往往较长．然而系统上的数据时刻都在增长，更新推荐期间会产生大量数据，这些新数据对下一刻的推荐有较大的利用价值，系统却无法及时利用起来．为了能及时的利用这些新数据来提高推荐系统的推荐质量，提出一种数据递增式的混合推荐方法．该模型主要分为离线计算模块和在线推荐模块，离线模块用于计算出个性化推荐列表，在线推荐模根据递增的实时数据维护一个流行趋势动量表，然后结合两个模块的结果给出匿名推荐或者个性化推荐．实验证明，该方法简单、有效、可行，能较好的改善推荐系统性能．

New Approach to Hybrid Recommendation Based on Incremental Data

Due to the large amount of training data and the high complexity of its recommend algorithm, the updating cycle of recommendation system tend to be long. However, the data on the system is growing all the time, and a lot of data is produced during the cycle, which is useful for the recommendation of next moment, and recommendation system can't use these data in time. In order to use these data in time to improve the quality of recommendation system, a new approach to hybrid recommendation based on incremental data was proposed. The approach mainly divided recommendation into offline and online module, the offline module is used to produce the personalized recommendation list, while the online recommendation module maintains a list of popular trend momentum based on real-time and incremental data. Then, combining with the results of the two modules, based on which give users anonymous or personalized recommendation. Experiments show that the approach is simple, effective, feasible, and can improve the performance of recommendation system better.