欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
为了克服传统协同过滤推荐技术的局限，提出了一种基于CTM．PMF模型的物品推荐方法。在PMF模型的基础上，引入CTM模型，将PMF模型良好的推荐品质和CTM模型优越的物品表示方法相结合，有效地实现了新物品推荐；通过引入用户兴趣因子，解决了用户对已购买物品的兴趣变化问题。在自建的物品数据集上，利用提出的方法、PMF模型、G．PLSA模型和UBCF方法进行了对比实验，实验结果表明该方法具有良好的物品推荐品质。
英文摘要：
In order to overcome the shortcomings of the traditional collaborative filtering method, a new recommendation method based on CTM-PMF （Correlated Topic Model and Probabilistic Matrix Factorization） model is proposed. CTM-PMF model combines the PMF model in collaborative filtering and the CTM model in topic modes. The result of this combination is that both advantages of PMF model and advantages of CTM model are combined, solves the problem of new items. In this method, a factor is also introduced which reflects the change of user＇ s interest on the products bought previously. The algorithm is tested on a self-build product dataset. The experimental results show that the new method has better capacity for recommendation compared to PMF, G-PLSA and UBCF method.
同期刊论文项目
同项目期刊论文
Measuring and Forecasting Volatility in Chinese Stock Market Using HAR-CJ-M Model (全球Top1% ESI 高被引论文
Forecasting Return Volatility of the CSI 300 Index Using the Stochastic Volatility Model with Contin
Modified Liu–Storey Type Nonlinear Conjugate Gradient Method for Large-Scale Unconstrained Optimizat
Comments on “A Hybrid Conjugate Gradient Method Based on a Quadratic Relaxation of the Dai-Yuan Hybr
Modi?ed Liu-Storey type nonlinear conjugate gradient method for large-scale unconstrained optimizati
Research on investor sentiment effect on A-Share market in China based on analysis of A-Share materi
期刊信息
位置：