Quantum Nearest Neighbor Collaborative Filtering Algorithm for Recommendation System

• DOI: 10.1145/3674982
• Publication Date: 2024-06-29T10:52:54Z
• Authors: Jiaye Li; Jinjing Shi; Jian Zhang; Yuhu Lu; Qin Li; Chunlin Yu; Shichao Zhang
• Keywords: Nearest-neighbor chain algorithm; Best bin first

Recommendation has become especially crucial during the COVID-19 pandemic as a significant number of people rely on online shopping from home. Existing recommendation algorithms, designed to address issues like cold start and data sparsity, often overlook time constraints users. Specifically, users expect receive recommendations for products interest in shortest possible time. To this challenge, we propose novel collaborative filtering algorithm that leverages advantages quantum computing circuits based reconstruction. This approach allows rapid identification similar target user, thereby improving speed. In our method, utilize information known linearly reconstruct users, forming relational matrix. Subsequently, employ \(l_{2,1}-\) norm \(l_{1}-\) sparsely constrain relationship matrix, deducing weight each user. The final step involves providing these weights. Furthermore, implement proposed using circuit, enabling exponential acceleration. matrix is derived state outputted by circuit. speed process theoretically demonstrated detail. Experimental results indicate outperforms state-of-the-art methods terms root mean squared error (RMSE), absolute (MAE) normalized discounted cumulative gain (NDCG). Compared comparison achieves fastest across eight public datasets.