# Hybrid Sparrow Clustered (HSC) Algorithm for Top-N Recommendation System

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## Abstract

**:**

## 1. Introduction

## 2. Related Work

## 3. Materials and Methods

#### 3.1. Clustering Using Sparrow Search

- (1)
- The producers usually have a lot of energy and give scavengers places or directions to look for food. They are in charge of finding places where a lot of food can be found. The amount of energy each person has is based on how well they are thought of by others.
- (2)
- As soon as the sparrow sees the predator, the birds start to chirp as a warning. To keep people safe, producers have to lead all scavengers to the safe area when the alarm value is higher than the safety level.
- (3)
- Each sparrow can become a producer as long as it looks for the best places to get food, but the overall population doesn’t change.
- (4)
- The sparrows with the most energy would be the ones who made the food. Several people who are starving are more likely to fly to other places to get food so that they can have more energy to do things.
- (5)
- The scavengers follow the person who can give them the best food as they look for food. In the meantime, some scavengers may keep an eye on the people who make the food and compete with each other to get more.
- (6)
- The sparrows at the edge of the group move quickly to a safe area when they sense danger. The sparrows in the middle move around so they can be near other people.

Algorithm 1. Algorithm fir clustering using sparrow search |

Input |

• data: the dataset to be clustered |

• K: the number of clusters to be generated |

• N: the number of sparrows |

• P: the number of producers |

• S: the number of sparrow who sense the danger |

• G: the maximum iterations |

Algorithm |

1. Create N sparrow, each having K cluster heads with random normalized values. |

2. Create clusters of data for each sparrow (based on shortest Euclidean distance). |

3. Calculate the fitness of each sparrow. |

4. Rank the sparrows and find the current best and cueeent worst sparrow. |

5. for $i=1$ to P: update sparrow location using Equation (1). |

6. for $i=P+1$ to N: update sparrow location using Equation (2). |

7. for i = 1 to S: update sparrow location using Equation (3). |

8. Change the location of each sparrow if it is better than the old location. |

9. Repeat the step 3–8 for G iterations. |

10. Return the cluster- heads of the fittest sparrow. |

Output: Optimal clusters |

#### 3.2. Generating Recommendation by Using Sparrow Clustered Recommendation System

## 4. Results

#### 4.1. Phase I: Training Phase

#### 4.2. Phase II: Process of Recommendation for Active Users

#### 4.2.1. Mean Absolute Error (MAE)

#### 4.2.2. Standard Deviation (SD)

#### 4.2.3. Root Mean Square Error (RMSE)

#### 4.2.4. t-Value

#### 4.2.5. Precision

#### 4.2.6. Recall

- MovieLens 100,000—an original dataset composed from the MovieLens website (movielens.umn.edu, accessed on 4 May 2022) in a time frame of 7 months (i.e., from 19 September 1997 to 22 April 1998) consists of 100,000 ratings of 1682 movies from 943 users. Rating scale ranges between 1 and 5. Data is easily available for experimental computation and analysis.
- MovieLens 1 million—This Dataset consists of four features: userID, MovieID, rating, and timestamp. It includes 6040 MovieLens users who provided 1,000,209 ratings of 3900 movies. With a rating scale of 1–5 stars, every single user provides a minimum of 20 ratings.
- Jester—Jester is a dataset of 59,132 users and 150 jokes, and consists of 1.7 million ratings ranging between −10 and 10. It is widely available and is often used for experimental examination of collaborative filtering recommender systems. The dataset features include [userid], [itemid] and [rating]. University of California, Berkeley created this dataset, and the data is associated with an online joke-recommendation system.
- Epinion—A total of 664,824 ratings of 139,738 items from 49,290 users constitutes the Epinion dataset. Open-source data composed from Epinion.com includes userid, itemid, and rating. A scale is set ranging from 1–5 for rating individual items. The performance of the proposed sparrow-based recommendation system is based on a different cluster size.

## 5. Conclusions and Future Scope

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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Movie #1 | Movie #2 | . | . | . | Movie #1682 | |
---|---|---|---|---|---|---|

User #1 | 0 | 3 | . | . | . | 0 |

User #2 | 4 | 0 | . | . | . | 2 |

. | . | . | . | . | . | . |

. | . | . | . | . | . | . |

. | . | . | . | . | . | . |

User #943 | 0 | 5 | . | . | . | 0 |

Movie #1 | Movie #2 | . | Movie #1682 | ||
---|---|---|---|---|---|

Sparrow #1 | Cluster-Head #1 | 2 | 3 | . | 3 |

Cluster-Head #2 | 3 | 1 | . | 5 | |

Cluster-Head #3 | 4 | 1 | . | 2 | |

. | . | . | . | . | . |

. | . | . | . | . | . |

Sparrow #20 | Cluster-Head #1 | 5 | 1 | . | 1 |

Cluster-Head #2 | 1 | 2 | . | 2 | |

Cluster-Head #3 | 2 | 4 | . | 4 |

**Table 3.**Sample of cluster assigned to each user in fittest sparrow (assuming that there are 3 clusters in each sparrow).

User #1 | User #2 | User #3 | . | User #943 | |
---|---|---|---|---|---|

Cluster # | 3 | 1 | 3 | . | 2 |

**Table 4.**Performance of sparrow-search recommender system based on different cluster size on MovieLens 100 k dataset.

S. No. | No. of Clusters (k) | MAE | SD | RMSE | t-Value | Recall | Precision |
---|---|---|---|---|---|---|---|

1 | 10 | 0.785 | 0.179 | 1.282 | 3.348 | 0.329 | 0.311 |

2 | 20 | 0.776 | 0.126 | 1.280 | 2.814 | 0.378 | 0.361 |

3 | 30 | 0.765 | 0.124 | 1.274 | 2.791 | 0.443 | 0.426 |

4 | 40 | 0.744 | 0.133 | 1.258 | 2.816 | 0.491 | 0.452 |

5 | 50 | 0.729 | 0.125 | 1.248 | 2.816 | 0.534 | 0.498 |

6 | 60 | 0.712 | 0.112 | 1.239 | 2.816 | 0.567 | 0.538 |

7 | 70 | 0.695 | 0.117 | 1.229 | 2.814 | 0.604 | 0.552 |

NMF | SVD | Firefly | Cuckoo | Whale | Sparrow | |
---|---|---|---|---|---|---|

MAE | 0.758 | 0.737 | 0.695 | 0.697 | 0.691 | 0.685 |

SD | 0.123 | 0.121 | 0.117 | 0.119 | 0.115 | 0.113 |

RMSE | 0.963 | 0.934 | 1.229 | 1.231 | 1.228 | 1.220 |

t-value | 2.818 | 2.816 | 2.814 | 2.816 | 2.811 | 2.792 |

Recall | 0.594 | 0.597 | 0.604 | 0.592 | 0.610 | 0.640 |

Precision | 0.546 | 0.549 | 0.552 | 0.554 | 0.560 | 0.602 |

NMF | SVD | Firefly | Cuckoo | Whale | Sparrow | |
---|---|---|---|---|---|---|

MAE | 0.724 | 0.686 | 0.664 | 0.678 | 0.660 | 0.654 |

SD | 0.123 | 0.121 | 0.117 | 0.119 | 0.115 | 0.113 |

RMSE | 0.916 | 0.873 | 0.852 | 0.862 | 0.857 | 0.841 |

t-value | 2.814 | 2.809 | 2.818 | 2.811 | 2.803 | 2.784 |

Recall | 0.602 | 0.614 | 0.638 | 0.622 | 0.648 | 0.673 |

Precision | 0.531 | 0.526 | 0.588 | 0.554 | 0.611 | 0.645 |

NMF | SVD | Firefly | Cuckoo | Whale | Sparrow | |
---|---|---|---|---|---|---|

MAE | 3.51 | 3.37 | 3.11 | 3.41 | 3.33 | 3.09 |

SD | 0.32 | 0.35 | 0.39 | 0.35 | 0.41 | 0.44 |

RMSE | 4.63 | 4.49 | 4.26 | 4.31 | 4.22 | 4.01 |

t-value | 3.11 | 3.04 | 2.98 | 3.02 | 2.96 | 2.91 |

Recall | 0.62 | 0.67 | 0.72 | 0.70 | 0.73 | 0.77 |

Precision | 0.68 | 0.69 | 0.74 | 0.71 | 0.74 | 0.78 |

NMF | SVD | Firefly | Cuckoo | Whale | Sparrow | |
---|---|---|---|---|---|---|

MAE | 1.06 | 0.96 | 0.87 | 0.91 | 0.84 | 0.78 |

SD | 0.28 | 0.31 | 0.35 | 0.32 | 0.36 | 0.39 |

RMSE | 1.38 | 1.26 | 1.15 | 1.23 | 1.10 | 1.02 |

t-value | 2.09 | 1.98 | 1.81 | 1.88 | 1.82 | 1.74 |

Recall | 0.65 | 0.69 | 0.74 | 0.71 | 0.74 | 0.76 |

Precision | 0.71 | 0.73 | 0.74 | 0.73 | 0.75 | 0.77 |

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## Share and Cite

**MDPI and ACS Style**

Sharma, B.; Hashmi, A.; Gupta, C.; Khalaf, O.I.; Abdulsahib, G.M.; Itani, M.M.
Hybrid Sparrow Clustered (HSC) Algorithm for Top-N Recommendation System. *Symmetry* **2022**, *14*, 793.
https://doi.org/10.3390/sym14040793

**AMA Style**

Sharma B, Hashmi A, Gupta C, Khalaf OI, Abdulsahib GM, Itani MM.
Hybrid Sparrow Clustered (HSC) Algorithm for Top-N Recommendation System. *Symmetry*. 2022; 14(4):793.
https://doi.org/10.3390/sym14040793

**Chicago/Turabian Style**

Sharma, Bharti, Adeel Hashmi, Charu Gupta, Osamah Ibrahim Khalaf, Ghaida Muttashar Abdulsahib, and Malakeh Muhyiddeen Itani.
2022. "Hybrid Sparrow Clustered (HSC) Algorithm for Top-N Recommendation System" *Symmetry* 14, no. 4: 793.
https://doi.org/10.3390/sym14040793