Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor Networks

Abstract

Collaborative filtering models generally perform better than content-based filtering models and do not require careful feature engineering. However, in the cold-start scenario collaborative information may be scarce or even unavailable, whereas the content information may be abundant, but also noisy and expensive to acquire. Thus, selection of particular features that improve cold-start recommendations becomes an important and non-trivial task. In the recent approach by Nembrini et al., the feature selection is driven by the correlational compatibility between collaborative and content-based models. The problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) which, due to its NP-hard complexity, is solved using Quantum Annealing on a quantum computer provided by D-Wave. Inspired by the reported results, we contend the idea that current quantum annealers are superior for this problem and instead focus on classical algorithms. In particular, we tackle QUBO via TTOpt, a recently proposed black-box optimizer based on tensor networks and multilinear algebra. We show the computational feasibility of this method for large problems with thousands of features, and empirically demonstrate that the solutions found are comparable to the ones obtained with D-Wave across all examined datasets. hardware and algorithmic aspects towards practical quantum solutions. The key driver for their innovation efforts is the technology’s promise for substantial computational gains across multiple domains: information retrieval, financial analysis, cryptography, and more. Are Quantum Computers Practical Yet? A Case Feature Tensor optimization method can run in reasonable time, even for large problems with thousands of unknowns, while achieving competitive results. All in all, our study provides a new angle on the attractiveness of state-of-the-art quantum-based solutions for recommender systems vs. its classical counterparts.