Personalized recommendation method based on both tag and time factors
一种综合标签和时间因素的个性化推荐方法

To solve the shortcomings of available user interests modeling methods in existing social tagging systems, this paper proposed a resources recommendation model integrated tag and its time information(TTRR). In this model, it consided the temporal characteristics of the user's interest, that user's preference for products were drifting over time, and the tags that users recently used play a vital to better reflect the recent interest of the user. So, based on the thought of collaborative filtering method, this paper utilized the tag frequency and item's launch time information to construct a pseudo rating matrix. Then it obtained the nearest neighbor sets of the active user based on the pseudo rating matrix. Finally, according to the nearest neighbor sets, it obtained the recommendation results. The experimental results conducted on the CiteULike data sets show that compared with the traditional recommendation method which are based on user's log behaviors, the TTRR model can effectively reflect the user's preferences and significantly improve the recommendation accuracy.