Tdctfic: A Novel Recommendation Framework Fusing Temporal Dynamics, Cnn-Based Text Features And Item Correlation

The traditional recommendation system (RS) can learn the potential personal preferences of users and potential attribute characteristics of items through the rating records between users and items to make recommendations. However, for the new items with no historical rating records, the traditional RS usually suffers from the typical cold start problem. Additional auxiliary information has usually been used in the item cold start recommendation, we further bring temporal dynamics, text and relevance in our models to release item cold start. Two new cold start recommendation models TmTx(Time, Text) and TmTI(Time, Text, Item correlation) proposed to solve the item cold start problem for different cold start scenarios. While well-known methods like TimeSVD++ and CoFactor partially take temporal dynamics, comments, and item correlations into consideration to solve the cold start problem but none of them combines these information together. Two models proposed in this paper fused features such as time, text, and relevance can effectively improve the performance under item cold start. We select the convolutional neural network (CNN) to extract features from item description text which provides the model the ability to deal with cold start items. Both proposed models can effectively improve the performance with item cold start. Experimental results on three real-world data set show that our proposed models lead to significant improvement compared with the baseline methods.