Research Article
Multimedia Recommendation System for Video Game Based on High-Level Visual Semantic Features
| | Input: the user set U, the multimedia item set I, the item description document C, the one-hot coding user rating features, and product images URLs. | | | Epoch, the iterations for each training | | (1) | Visual semantic feature learning (V (VSF) = visual semantic feature learning I (V, UR, and P)) | | (2) | Using the trained model, extract Visual Features of each image of items using the RCNN model | | (3) | User profile expansion (user profile expansion I (VSF, UR, and P)) | | | Extract the textual attributes of the product using description genre tags and product details | | (i) | Perform NLP tasks to remove noisy words | | (ii) | Stemming | | (iii) | TF-ID: to remove high frequency and stop words | | (4) | The given recorded data contain raw features, which contain categorial, highly sparse, and dense I (VSF, UR, and UPE) | | (5) | Recommendation using Deepfm | | | Output: the recommendation for the targeted user |
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