# An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review

#### 2.1. The Recommendation System and Hybrid Algorithm

#### 2.2. Iterative Design

## 3. Model Formulation

#### 3.1. The Solution Framework

#### 3.2. Obtain the Recommendation List

#### 3.2.1. CBF Algorithm

#### 3.2.2. CF Algorithm

#### 3.2.3. Association Rules-Based Algorithm

#### 3.3. Measure the Weights of Each Recommendation Result

- Step 1.
- Let $A=\{{a}_{1},\dots ,{a}_{i},\dots {a}_{\left|A\right|}\}$ be the set of all input attributes in recommended data sources $I$, and let $RC$ be the real recommended products. $\left|CES\right|$ is the number of recommendation algorithms.
- Step 2.
- Train the neural network $N$ to maximize the network accuracy with $A$ as input and $RC$ as output.
- Step 3.
- For $j=1,2,\dots ,\left|CES\right|$, let ${N}_{j}$ be a network whose connection weights are as follows:
- (a)
- For all the inputs except $\{{a}_{j,1},{a}_{j,2},\dots {a}_{j,}{}_{\left|SAj\right|}\}$, assign the connection weights of ${N}_{j}$ equal to the weights of $N$.
- (b)
- Set the connection weights of $\{{a}_{j,1},{a}_{j,2},\dots {a}_{j,}{}_{\left|SAj\right|}\}$ to zero.

- Step 4.
- Compute the influence of $S{A}_{j}$ to the network accuracy.
- Step 5.
- If $j\ge \left|CES\right|$, go to Step 6, otherwise, set $j=j+1$ and go to Step 3.
- Step 6.
- The derived $\{{w}_{1},\dots ,{w}_{j},\dots {w}_{\left|CES\right|}\}$ are the weights of the recommendation algorithms.

#### 3.4. Fuse the Results

## 4. Experimental Results

#### 4.1. Data Preparation

#### 4.2. The Consumer Coverage

#### 4.3. The Consumer Discovery Accuracy

#### 4.4. The Recommendation Recall

#### 4.5. The Recommendation Speed

## 5. Conclusions and Discussion

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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Algorithms | Accuracy | Automaticity | Real-Time | Diversity | Scalability | Cold-Start Problem | Sparsity Problem | |
---|---|---|---|---|---|---|---|---|

CBF | Inferior | Good | Good | Bad | Bad | New users | Not | |

CF | User-based CF | Better | Bad | Bad | Better | Bad | Serious | Serious |

Item-based CF | Better | Inferior | Inferior | Better | Bad | Serious | Serious | |

Association rules | General | Good | Good | Good | General | New projects | General |

Product | Algorithm | Recommend | Don’t Recommend | Uncertain |
---|---|---|---|---|

A | CBF | 65% | 30% | 5% |

CF | 67% | 30% | 3% | |

Association rules | 63% | 34% | 3% | |

B | CBF | 66% | 32% | 2% |

CF | 65% | 30% | 5% | |

Association rules | 67% | 29% | 4% | |

C | CBF | 56% | 40% | 4% |

CF | 58% | 30% | 12% | |

Association rules | 62% | 33% | 5% |

Product | Recommend | Don’t Recommend | Uncertain | Algorithm | Weights |
---|---|---|---|---|---|

A | 0.65 | 0.30 | 0.05 | CBF | 0.37 |

B | 0.66 | 0.32 | 0.02 | ||

C | 0.56 | 0.40 | 0.04 | ||

A | 0.67 | 0.30 | 0.03 | CF | 0.51 |

B | 0.65 | 0.30 | 0.05 | ||

C | 0.58 | 0.30 | 0.12 | ||

A | 0.63 | 0.34 | 0.03 | Association rules | 0.12 |

B | 0.67 | 0.29 | 0.04 | ||

C | 0.62 | 0.33 | 0.05 |

Product | Recommend | Don’t Recommend | Uncertain |
---|---|---|---|

A | 0.658 | 0.305 | 0.037 |

B | 0.656 | 0.306 | 0.038 |

C | 0.577 | 0.341 | 0.082 |

Algorithm | Data Statistics Time (24 H × 7) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|

1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |

proposed algorithm | 18% | 28% | 36% | 43% | 51% | 60% | 67% | 73% | 80% | 79% |

user-based CF | 16% | 22% | 29% | 37% | 44% | 52% | 59% | 61% | 70% | 65% |

association rules | 17% | 27% | 34% | 41% | 50% | 58% | 65% | 70% | 78% | 75% |

CBF | 17% | 25% | 32% | 39% | 48% | 57% | 64% | 68% | 77% | 72% |

item-based CF | 16% | 24% | 31% | 38% | 47% | 55% | 61% | 65% | 73% | 68% |

Algorithm | Number of Customers | |||||||||
---|---|---|---|---|---|---|---|---|---|---|

50 | 100 | 150 | 200 | 250 | 300 | 350 | 400 | 450 | 500 | |

proposed algorithm | 20% | 35% | 50% | 70% | 75% | 81% | 78% | 75% | 70% | 67% |

CBF | 17% | 27% | 41% | 60% | 64% | 71% | 61% | 52% | 48% | 41% |

item-based CF | 18% | 33% | 48% | 67% | 71% | 79% | 71% | 65% | 59% | 55% |

user-based CF | 18% | 32% | 47% | 66% | 68% | 77% | 69% | 61% | 54% | 49% |

association rules | 17% | 30% | 44% | 63% | 67% | 75% | 65% | 58% | 51% | 44% |

Algorithm | Number of Customers | |||||||||
---|---|---|---|---|---|---|---|---|---|---|

50 | 100 | 150 | 200 | 250 | 300 | 350 | 400 | 450 | 500 | |

proposed algorithm | 18% | 26% | 35% | 41% | 45% | 48% | 45% | 43% | 41% | 39% |

association rules | 17% | 24% | 33% | 39% | 41% | 45% | 42% | 38% | 34% | 29% |

item-based CF | 17% | 22% | 30% | 35% | 39% | 42% | 40% | 35% | 31% | 25% |

CBF | 17% | 22% | 29% | 35% | 38% | 42% | 39% | 35% | 31% | 25% |

user-based CF | 16% | 21% | 29% | 34% | 38% | 42% | 39% | 34% | 30% | 24% |

Customers | Proposed Algorithm (s) | CBF (s) | Association Rules (s) | Item-Based CF (s) | User-Based CF (s) |
---|---|---|---|---|---|

50 | 0.33 | 0.28 | 0.29 | 0.29 | 0.31 |

100 | 0.81 | 0.49 | 0.49 | 0.51 | 0.54 |

150 | 1.23 | 0.71 | 0.78 | 0.79 | 0.81 |

200 | 1.78 | 0.99 | 1.02 | 1.04 | 1.18 |

300 | 2.45 | 1.48 | 1.51 | 1.54 | 1.64 |

350 | 2.79 | 1.76 | 1.81 | 1.85 | 1.96 |

400 | 3.21 | 2.01 | 2.04 | 2.16 | 2.23 |

450 | 3.78 | 2.21 | 2.47 | 2.56 | 2.67 |

500 | 4.44 | 2.52 | 2.81 | 2.94 | 2.99 |

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**MDPI and ACS Style**

Guo, Y.; Wang, M.; Li, X.
An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model. *Symmetry* **2017**, *9*, 216.
https://doi.org/10.3390/sym9100216

**AMA Style**

Guo Y, Wang M, Li X.
An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model. *Symmetry*. 2017; 9(10):216.
https://doi.org/10.3390/sym9100216

**Chicago/Turabian Style**

Guo, Yan, Minxi Wang, and Xin Li.
2017. "An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model" *Symmetry* 9, no. 10: 216.
https://doi.org/10.3390/sym9100216