A Survey on Quantum Computing for Recommendation Systems
Abstract
:1. Introduction
2. Quantum Approaches for Recommender Systems
2.1. Quantum Computing for Matrix Decomposition
2.1.1. Singular Value Decomposition
2.1.2. Normalized Non-Negative Models
2.2. Quantum Computing for Clustering
2.3. Quantum Computing for Searching
2.4. Quantum Computing and Swarm Intelligence
2.5. Adiabatic Approaches to Quantum Computing
2.6. Quantum Computing-Inspired Approaches
2.7. Quantum Computing for Monte Carlo Simulation
3. Datasets Used in the Literature
Year | Paper | Dataset | Reference |
---|---|---|---|
2019 | Sawerwain et al. [43] | OMBd | [44] |
2019 | Andreev et al. [68] | Inria Graz-02 [69,70] | [40] |
2021 | Kolhe et al. [7] | Amazon [60,71] | [65] |
2021 | Ferrari et al. [54] | Netflix Prize [56] | [66] |
2021 | Wang et al. [27] | Yahoo! Webscope | [72] |
2022 | Pan et al. [59] | Citeulike [8] | [58,67] |
2022 | Ouedrhiri et al. [36] | MovieLens | [38] |
4. Challenges
- Computational Cost: The advantage of using quantum techniques is that these techniques can sensibly reduce computational costs and speed up processing. The paper of Kerenidis et al. focuses on the computational cost of their approach, and they assess their technique as , with m the number of users and n the number of products. k is the low-rank approximation for the global matrix. The technique is also run with constant memory. The SVT algorithm used in Ref. [33] has a cost that is , where is the condition number and depends on the value of the highest and lowest eigenvalue, and is the accuracy of the approximation.Wang et al. [27] adopt a recommendation through a third-order tensor of dimension N. The time complexity of the algorithm is , with k being the low-rank value and N taking into account the number of users, the items, and the number of different contexts. The Tang algorithm in Ref. [25] performs the recommendation with a time cost of .
- Performance The performance of these methodologies is somehow challenging to assess. A metric for evaluating the efficacy of the prediction was to consider the recommendation as a retrieval task and, therefore, use the accuracy, precision, recall, and F-measures. In [7], the average accuracy on the subset of the Amazon data [65] was 71%, the average precision 73.5%, the average recall 70.6%, and the average F-measure 71.5%. In Ref. [18], the precision is evaluated on a different dataset. Keeping constantly in mind that a direct comparison is not fair and that the presented technique outperformed similar techniques, the value for the Netflix [56] and Movielens [38] dataset is less than 10%. The evaluation is performed in Ref. [59] by computing a mean square error among the QINR method and a set of baseline methods. The average MSE for the different Amazon sets [65] was 0.97, outperforming the baseline techniques. In Ref. [36], the results are evaluated according to precision, recall, F1-measure, root mean square error(RMSE), and mean absolute error (MAE). The technique was tested on the dataset in Ref. [38]: precision was , recall , F-measure , RMSE and MAE .
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
CQFS | Collaborative-Driven Quantum Feature Selection |
DLT | Distributed Ledger Technologies |
ELBO | Evidence Lower Bound |
HHL | Harrow Hassidim Lloyd |
LDA | Latent Dirichlet Allocation |
MAE | Mean Absolute Error |
MGWO | Modified Grey Wolf Optimizer |
MWO | Mussels Wandering Optimization |
PCA | Principal Components Analysis |
QCS | QUBO Carousel Selection |
QICE | Quantum-Induced Clustering Ensemble |
QIPFCM | Quantum Possibilistic fuzzy C-means |
QMC | Quantum Monte Carlo |
QPO | Quantum Processing Unit |
QPSO | Quantum-Behaved Particle Swarm Optimization |
QUBO | Quadratic Unconstrained Binary Optimization |
RMSE | Root Mean Square Error (RMSE) |
RS | Recommendation Systems |
SQB | Standard Quantum-Based Similarity |
SVT | Singular Value Thresholding |
VBAE | Variational Bandwidth Auto-Encoder |
WOTS | Winternitz One-Time Signature |
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Pilato, G.; Vella, F. A Survey on Quantum Computing for Recommendation Systems. Information 2023, 14, 20. https://doi.org/10.3390/info14010020
Pilato G, Vella F. A Survey on Quantum Computing for Recommendation Systems. Information. 2023; 14(1):20. https://doi.org/10.3390/info14010020
Chicago/Turabian StylePilato, Giovanni, and Filippo Vella. 2023. "A Survey on Quantum Computing for Recommendation Systems" Information 14, no. 1: 20. https://doi.org/10.3390/info14010020
APA StylePilato, G., & Vella, F. (2023). A Survey on Quantum Computing for Recommendation Systems. Information, 14(1), 20. https://doi.org/10.3390/info14010020