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Quantum Nearest Neighbor Collaborative FilteringAlgorithm for Recommendation System
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DOI：10.1145/3674982.png)
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Recommendation has become especially crucial during the COVID-19 pandemic as a significant number ofpeople rely on online shopping from home. Existing recommendation algorithms, designed to address issueslike cold start and data sparsity, often overlook the time constraints of users. Specifically, users expect toreceive recommendations for products of interest in the shortest possible time. To address this challenge, wepropose a novel collaborative filtering recommendation algorithm that leverages the advantages of quantumcomputing circuits based on data reconstruction. This approach allows for the rapid identification of userssimilar to the target user, thereby improving recommendation speed. In our method, we utilize the informationof known users to linearly reconstruct that of the target users, forming a relational matrix. Subsequently, weemployk(2,1)-norm andl(1)-norm to sparsely constrain the relationship matrix, deducing the weight of eachknown user. The final step involves providing similar recommendations to target users based on these weights
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