Ranking Oriented Algorithm for Top-N Recommendation
Xue Liu · Jisuanji fangzhen · 2013
Recommender system uses customers' historical preference data to provide personalized recommendation.Top-N recommendation problem is that systems recommend to each user N items that they probably like most.To address this problem,we proposed a ranking oriented algorithm EIBRO-MF.Firstly we blended explicit and implicit feedback data to construct user-item preference pairs.Then we used the preference pairs to train a collaborate filtering model and obtained the recommendation results at last.Experiments with real-world data sets demonstate that the proposed algorithm can greatly improve the ranking precision of recommendation lists in constract with alternative methods like traditional CF-based algorithm and ranking algorithm.