2022/05/09 by Artyom Nikitin, Nikitin, Artyom, Andrei Chertkov +8 · 3 citations
Computer Science · Mathematics · Physics and Astronomy · #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Quantum Computing Algorithms and Architecture #Quantum many-body systems #Tensor decomposition and applications #cs.IR #cs.LG
paper · pdf · doi:10.48550/arxiv.2205.04490
Added affiliation. Fixed table references
openalex publication_date 2022/05/09 · arxiv created 2022/05/12 · arxiv updated 2022/05/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.