Extended Recommendation Framework: Generating the Text of a User Review as a Personalized Summary
Table 3: Test performance (classification error) as polarity classifiers. LL stands for LibLinear (SVM), μi, γu.γi, fA, fT are the recommender systems as in table 2. LL + fA and LL + fT are two hybrid opinion classification models combining the SVM classifier and fA and fT recommender systems.
['Subsets', 'LL', '[ITALIC] μi', '[ITALIC] γu. [ITALIC] γi', '[ITALIC] fA', '[ITALIC] fT', 'LL +  [ITALIC] fA', 'LL +  [ITALIC] fT']
Because they give very poor performance, the bias recommendation models (μ and μu) are not presented here. The item bias μi, second column, gives a baseline, which is improved by the matrix factorization γu.γi, third column. Our hybrid models fA, fourth column, and fT, fifth column, have lower classification errors than all the other recommender systems. The first column, LL is the linear support vector machine (SVM) baseline. It has been learnt on the training set texts, and the regularization hyperparameter has been selected using the validation set. Our implementation relies on liblinear (LL) The resulting hybrid approaches, denoted LL + fA and LL + fT, exploit both text based decision (SVM) and user profile (fA and fT).