A Top-N Movie Recommendation Framework Based on Deep Neural Network with Heterogeneous Modeling
Abstract
:1. Introduction
- We exploit both explicit and implicit feedback information to obtain the user’s preference information and the underlying characteristics of the item based on the meta-path selection results. Additionally, in order to obtain explicit feedback information, two bias factors are introduced according to the individual characteristics of the user–item information.
- We fuse MF and DNN to mine the potential features of users and items from both linear and nonlinear perspectives. MF and DNN learning are independently embedded to better capture user preference information and the potential feature information of items.
- Using the leave-one-out evaluation method, we combine explicit and implicit feedback results to obtain the top-N recommendation list for target users and adopt the HR and NDCG metrics to evaluate the proposed model.
2. Related Work
3. Our Approach: MFDNN
3.1. Problem Definition
3.2. Top-N Recommendation Architecture
3.3. Framework of MFDNN
3.4. Implementation of MFDNN
4. Experiments
4.1. Experimental Setup
4.1.1. Datasets
4.1.2. Evaluation Metrics
4.1.3. Baseline Methods
4.2. Parameters Analysis
4.3. Performance and Comparison
4.4. Discussions
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Symbol | Description |
---|---|
Y | User–item interaction matrix |
U | Set of users |
I | Set of items |
Final prediction results | |
Implicit feedback prediction results | |
Explicit feedback prediction results | |
User–item relation matrix | |
User–item rating matrix | |
Final prediction results of MF | |
Final prediction results of DNN | |
User embedding vector of MF | |
Item embedding vector of MF | |
User embedding vector of DNN | |
Item embedding vector of DNN |
Algorithm MFDNN algorithm |
User–item relation matrix; |
User–item rating matrix; |
Parameter of regularization term; |
Learning rate⇐0.001; |
epochs ⇐ Number of iterations; |
User embedding vector of MF; |
Item embedding vector of MF; |
User embedding vector of DNN; |
Item embedding vector of DNN; |
epochs Calculate Equations (8)–(10) |
Calculate Equations (11)–(14) |
Update MFDNN with Adam |
Calculate Equation (7) |
Calculate at the same way |
Calculate Equation (15) |
Top-N recommendation list |
Aspect | MovieLens 1m | Netflix |
---|---|---|
#users | 6040 | 48,018 |
#movies | 3706 | 17,770 |
#ratings | 1,000,209 | 11,160,900 |
Rating Density | 0.04468 | 0.01308 |
MovieLens 1m | Netflix | |||
---|---|---|---|---|
Method | HR@10 | NDCG@10 | HR@10 | NDCG@10 |
MFDNN | 0.7278 | 0.4319 | 0.6828 | 0.4214 |
DMF | 0.6735 | 0.3975 | 0.5776 | 0.3459 |
NCF | 0.7048 | 0.4252 | 0.6245 | 0.4000 |
HeteCF | 0.7097 | 0.4268 | 0.6601 | 0.4013 |
HeteMF | 0.7123 | 0.4271 | 0.6609 | 0.4062 |
CMF | 0.7235 | 0.4308 | 0.6445 | 0.3893 |
MovieLens 1m | Netfilx | |||
---|---|---|---|---|
Top-N | HR | NDCG | HR | NDCG |
N=5 | 0.5303 | 0.3672 | 0.4583 | 0.3058 |
N=10 | 0.7279 | 0.4319 | 0.6828 | 0.4214 |
N=15 | 0.7869 | 0.4443 | 0.7542 | 0.4386 |
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Gong, J.; Zhang, X.; Li, Q.; Wang, C.; Song, Y.; Zhao, Z.; Wang, S. A Top-N Movie Recommendation Framework Based on Deep Neural Network with Heterogeneous Modeling. Appl. Sci. 2021, 11, 7418. https://doi.org/10.3390/app11167418
Gong J, Zhang X, Li Q, Wang C, Song Y, Zhao Z, Wang S. A Top-N Movie Recommendation Framework Based on Deep Neural Network with Heterogeneous Modeling. Applied Sciences. 2021; 11(16):7418. https://doi.org/10.3390/app11167418
Chicago/Turabian StyleGong, Jibing, Xinghao Zhang, Qing Li, Cheng Wang, Yaxi Song, Zhiyong Zhao, and Shuli Wang. 2021. "A Top-N Movie Recommendation Framework Based on Deep Neural Network with Heterogeneous Modeling" Applied Sciences 11, no. 16: 7418. https://doi.org/10.3390/app11167418
APA StyleGong, J., Zhang, X., Li, Q., Wang, C., Song, Y., Zhao, Z., & Wang, S. (2021). A Top-N Movie Recommendation Framework Based on Deep Neural Network with Heterogeneous Modeling. Applied Sciences, 11(16), 7418. https://doi.org/10.3390/app11167418