Research Article
Word Sequential Using Deep LSTM and Matrix Factorization to Handle Rating Sparse Data for E-Commerce Recommender System
Table 2
The improvement of latent factors using contextual insight.
| Ref. | Method | Latent factor | Rating | Item side document | Bag of words | Deep learning category | AE | CNN | LSTM |
| [15] | PMF | √ | √ | — | — | — | — | — | [16] | LDA | √ | √ | √ | √ | — | — | — | [17] | CTR | √ | √ | √ | √ | — | — | — | [18] | CDL | √ | √ | √ | — | √ | — | — | [26] | SVD + AE | √ | √ | √ | — | √ | — | — | [20] | CNN + PMF | √ | √ | √ | — | — | √ | — | | (LSTM + PMF) | √ | √ | √ | — | — | — | √ |
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