Details

Autor(en) / Beteiligte

• Sharma, Bharti
• Hashmi, Adeel
• Gupta, Charu
• Khalaf, Osamah Ibrahim
• Abdulsahib, Ghaida Muttashar
• Itani, Malakeh Muhyiddeen

Titel

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

Ist Teil von

• Symmetry (Basel), 2022-04, Vol.14 (4), p.793

Ort / Verlag

Basel: MDPI AG

Erscheinungsjahr

2022

Quelle

Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals

Beschreibungen/Notizen

• Recommendation systems suggest relevant items to a user based on the similarity between users or between items. In a collaborative filtering approach for generating recommendations, there is a symmetry between the users. That is, if user A has similar interests with user B, then an item liked by B can be recommended to A and vice versa. To provide optimal and fast recommendations, a recommender system may generate and keep clusters of existing users/items. In this research work, a hybrid sparrow clustered (HSC) recommender system is developed, and is applied to the MovieLens dataset to demonstrate its effectiveness and efficiency. The proposed method (HSC) is also compared to other methods, and the results are compared. Precision, mean absolute error, recall, and accuracy metrics were used to figure out how well the movie recommender system worked for the HSC collaborative movie recommender system. The results of the experiment on the MovieLens dataset show that the proposed method is quite promising when it comes to scalability, performance, and personalized movie recommendations.