# Artificial Neural Networks and Particle Swarm Optimization Algorithms for Preference Prediction in Multi-Criteria Recommender Systems

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## Abstract

**:**

## 1. Introduction

## 2. Related Work

## 3. Recommender Systems

**2**on ${M}_{1}$, but ${U}_{3}$ and ${U}_{4}$ have different opinions about ${M}_{1}$. This is because ${U}_{3}$ gives little importance to the ratings of the second and fourth criteria, as the overall rating is closer the ratings of the first and third criteria. However, ${U}_{4}$ has a counter-consideration concerning the influence of the criteria ratings with respect to the overall rating.

## 4. Particle Swarm Optimization (PSO)

## 5. The Proposed Model and Approach

- Decompose the n-dimensional multi-criteria rating problem into n distinct single rating problems.
- Choose a prediction function or algorithm that can learn the relationships between the criteria ratings and the overall rating.
- Integrate the prediction algorithm with the distinct single rating techniques of step 1 for predicting the criteria ratings and the overall rating.
- Provide a list of recommendations.

## 6. Experimental Methodology

^{+}to F) were transformed to numbers from 13 to 1, respectively.

^{+}→13, A→12, A

^{−}→11, B

^{+}→10, …, D→3, D

^{−}→2, and F→1. Table 3 presents the transformed numerical ratings of Table 2. The dataset contains a total of 62,156 ratings of 6078 users for 976 movies. We also used Pearson correlation coefficient to measure the correlations between the criteria ratings and the overall rating. The percentage correlations for the action, direction, story, and he visual effects of the movies are approximately 86.5%, 91.1%, 90.5%, and 84.4% respectively. This shows the strength of the relationship between the criteria ratings and the overall rating. According to these calculated correlations, users are more interested in the story and direction of the movies than visual effects and actions of the movies.

- Single_U: A user-based kNN recommender system that computes similarities between users using Equation (3)
- Single_I: An item-based kNN recommender system that computes similarities between items using a modified version of Equation (3) to find similarities between item i and item j.
- MCRSs_Sim: A heuristic-based MCRS that computes ${r}_{o}$ based on the average similarities between users using Equation (12), where $si{m}_{k}(u,v)$ is the similarity between u and v based on the ${k}^{th}$ criterion that could be obtained using Equation (3). ${r}_{o}$ will be computed using Equation (5) (in this case ${r}_{o}$ = ${\widehat{r}}_{ui}$ while $sim(u,v)$ = $si{m}_{avg}$).$$si{m}_{avg}(u,v)=\frac{1}{n+1}\sum _{k=0}^{n}si{m}_{k}(u,v)$$
- ANNs_U: A model-based MCRSs that integrates PSO-based ANNs with Single_U in item 1 to estimate the overall rating. We named the rating provided by this model as ${\widehat{r}}_{ui}^{user}$.
- ANNs_I: A model-based MCRSs that integrates PSO-based ANNs with Single_I in item 2 to estimate the overall rating. We named the rating provided by this model as ${\widehat{r}}_{ui}^{item}$.
- ANNs_W: This approach combines items number 4 and 5 above in a weighted form [51] to estimate the overall rating as a weighted sum of the ratings from items 4 and 5 as given in Equation (11), where ${\omega}_{u}$ and ${\omega}_{i}$ are the weights that are estimated using the gradient descent algorithm [35].

- Mean average error (MAE)
- Root mean square error (RMSE)
- Precision, recall, and F-measure
- Fraction of concordant pairs (FCP)
- Normalized discounted cumulative gain (NDCG)
- Mean reciprocal ranking (MRR)
- Gini coefficient.

## 7. Results and Discussion

## 8. Conclusions and Future Work

## Author Contributions

## Conflicts of Interest

## References

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${\mathit{M}}_{1}$ | ${\mathit{M}}_{2}$ | ${\mathit{M}}_{3}$ | ${\mathit{M}}_{4}$ | … | ${\mathit{M}}_{\mathit{n}}$ | |
---|---|---|---|---|---|---|

${U}_{1}$ | 5_{4,5,3,5} | 5_{5,5,4,5} | 3_{4,2,3,3} | 3_{4,3,3,3} | … | 1_{2,1,3,1} |

${U}_{2}$ | 2_{4,1,3,1} | 5_{5,5,5,5} | 5_{5,4,4,4} | 3_{4,2,3,5} | … | 4_{4,3,3,5} |

${U}_{3}$ | 2_{1,5,2,4} | 5_{5,5,5,5} | 5_{4,5,4,4} | 3_{2,5,4,3} | … | 4_{3,5,4,3} |

${U}_{4}$ | 2_{4,1,5,1} | 2_{2,2,2,2} | 5_{5,4,3,4} | 3_{5,3,3,5} | … | 4_{5,2,3,5} |

. | ||||||

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${U}_{m}$ | 3_{3,3,3,2} | 2_{1,1,1,5} | 5_{5,4,5,4} | 4_{4,4,3,5} | … | 1_{2,1,1,1} |

User | Movie | Direction | Action | Story | Visual | Overall |
---|---|---|---|---|---|---|

ID | ID | $\left({\mathit{r}}_{1}\right)$ | $\left({\mathit{r}}_{2}\right)$ | $\left({\mathit{r}}_{3}\right)$ | $\left({\mathit{r}}_{4}\right)$ | $\left({\mathit{r}}_{\mathit{o}}\right)$ |

101 | 1 | ${A}^{+}$ | C | ${C}^{-}$ | ${B}^{-}$ | ${C}^{-}$ |

3 | B | ${B}^{+}$ | ${B}^{+}$ | ${A}^{-}$ | ${B}^{-}$ | |

5 | ${B}^{-}$ | ${A}^{-}$ | ${B}^{+}$ | ${A}^{-}$ | ${A}^{-}$ | |

102 | 3 | ${A}^{+}$ | ${A}^{+}$ | ${A}^{+}$ | ${A}^{+}$ | ${A}^{+}$ |

5 | ${C}^{-}$ | C | ${A}^{+}$ | ${A}^{+}$ | ${A}^{+}$ | |

6 | C | B | ${C}^{+}$ | ${B}^{-}$ | ${B}^{-}$ |

User | Movie | Direction | Action | Story | Visual | Overall |
---|---|---|---|---|---|---|

ID | ID | $\left({\mathit{r}}_{1}\right)$ | $\left({\mathit{r}}_{2}\right)$ | $\left({\mathit{r}}_{3}\right)$ | $\left({\mathit{r}}_{4}\right)$ | $\left({\mathit{r}}_{\mathit{o}}\right)$ |

101 | 1 | 13 | 6 | 5 | 8 | 5 |

3 | 9 | 10 | 10 | 11 | 8 | |

5 | 8 | 11 | 10 | 11 | 11 | |

102 | 3 | 13 | 13 | 13 | 13 | 13 |

5 | 5 | 6 | 13 | 13 | 13 | |

6 | 6 | 9 | 7 | 8 | 8 |

**Table 4.**Evaluation results. FCP: fraction of concordant pairs; MRR: mean reciprocal ranking; NDCG: normalized discounted cumulative gain.

Size of K | Single_I | Single_U | MCRSs_Sim | ANNs_I | ANNs_U | ANNs_W |
---|---|---|---|---|---|---|

RMSE | 3.030 | 2.973 | 2.300 | 2.081 | 2.066 | 1.945 |

MAE | 2.169 | 2.075 | 1.575 | 1.448 | 1.436 | 1.353 |

Precision | 0.795 | 0.802 | 0.817 | 0.828 | 0.835 | 0.838 |

Recall | 0.800 | 0.805 | 0.810 | 0.821 | 0.824 | 0.833 |

F_{1} | 0.797 | 0.803 | 0.813 | 0.824 | 0.829 | 0.835 |

MRR × 10^{−2} | 0.109 | 0.122 | 0.166 | 0.280 | 0.170 | 0.570 |

NDCG | 0.887 | 0.902 | 0.925 | 0.961 | 0.921 | 0.975 |

Gini | 0.739 | 0.768 | 0.832 | 0.836 | 0.839 | 0.858 |

FCP | 0.620 | 0.641 | 0.738 | 0.816 | 0.822 | 0.839 |

Actual | Single_I | Single_U | MCRSs_Sim | ANNs_I | ANNs_U | ANNs_W | |
---|---|---|---|---|---|---|---|

Actual | 1.000 | 0.834 | 0.739 | 0.894 | 0.916 | 0.896 | 0.935 |

Single_I | 0.834 | 1.000 | 0.664 | 0.921 | 0.956 | 0.759 | 0.891 |

Single_U | 0.739 | 0.664 | 1.000 | 0.666 | 0.737 | 0.872 | 0.793 |

MCRSs_Sim | 0.894 | 0.921 | 0.666 | 1.000 | 0.968 | 0.794 | 0.903 |

ANNs_I | 0.916 | 0.956 | 0.737 | 0.968 | 1.000 | 0.814 | 0.943 |

ANNs_U | 0.896 | 0.759 | 0.872 | 0.794 | 0.814 | 1.000 | 0.922 |

ANNs_W | 0.935 | 0.891 | 0.793 | 0.903 | 0.943 | 0.922 | 1.000 |

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**MDPI and ACS Style**

Hamada, M.; Hassan, M.
Artificial Neural Networks and Particle Swarm Optimization Algorithms for Preference Prediction in Multi-Criteria Recommender Systems. *Informatics* **2018**, *5*, 25.
https://doi.org/10.3390/informatics5020025

**AMA Style**

Hamada M, Hassan M.
Artificial Neural Networks and Particle Swarm Optimization Algorithms for Preference Prediction in Multi-Criteria Recommender Systems. *Informatics*. 2018; 5(2):25.
https://doi.org/10.3390/informatics5020025

**Chicago/Turabian Style**

Hamada, Mohamed, and Mohammed Hassan.
2018. "Artificial Neural Networks and Particle Swarm Optimization Algorithms for Preference Prediction in Multi-Criteria Recommender Systems" *Informatics* 5, no. 2: 25.
https://doi.org/10.3390/informatics5020025