Research Article
A Time-Aware CNN-Based Personalized Recommender System
Algorithm 1
Time-aware CNN-based personalized recommendation algorithm.
| Input: users.dat, items.dat, ratings.dat with timestamp Rt, target user u, user u’s time context t | | Output: recommended item list for u RL | | Step 1: process data and save processed data to preprocess.p; | | Step 2: open preprocess.p and set parameters; | | Step 3: construct NN and generate users’ features and items’ features; | | Step 4: construct graph to calculate prediction rating by user similarity calculation, update parameter settings according to MSE; | | Step 5: randomly split dataset into training set and test set, and then train NN; | | Step 6: save trained model and parameters; | | Step 7: load saved model to recommend for target user u according to t; | | Step 8: generate recommendation list RL from time-aware CNN-based personalized recommendation algorithm; | | Step 9: return RL. |
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