Search papers, labs, and topics across Lattice.
This paper introduces DualSpectralCF, a training-free framework that integrates explicit negative feedback into spectral collaborative filtering, enhancing recommendation accuracy without the need for gradient-based training. By utilizing a signed input signal and a signed item-item operator, the method effectively combines positive and negative user interactions, leading to significant performance improvements across various datasets. The results demonstrate that DualSpectralCF consistently outperforms traditional unsigned methods, achieving Recall@20 improvements of up to +32.6% while being substantially faster than existing alternatives like SIGformer.
Leveraging explicit dislikes, DualSpectralCF boosts recommendation accuracy by up to 32.6% without any training overhead.
Real-world recommendation platforms routinely collect explicit negative feedback such as 1-star reviews, hate-button clicks, distrust between users, and very-low watch-ratio videos. Learned sign-aware recommenders exploit this signal for clear accuracy gains, but only at the cost of gradient-based training. In parallel, a line of training-free spectral collaborative filtering methods matches or beats learned graph recommenders at a fraction of the cost, yet operates on positive interactions alone. We bridge these two lines with DualSpectralCF, a training-free framework of two components that attach to any spectral backbone of the form $\hat{\mathbf{r}}_u = F(\mathbf{M}) \mathbf{r}_u$: a signed input signal $\mathbf{r}_u^{\pm}$ that encodes the user's explicit dislikes, and a signed item-item operator $\mathbf{M}^{\pm}$ that blends like-together and dislike-together similarity. The framework is backbone-agnostic and adds just two scalar hyperparameters. We instantiate DualSpectralCF on ChebyCF, GF-CF, and Turbo-CF, and evaluate on five sign-aware benchmarks: every instance matches or beats its unsigned backbone on all 5 datasets, with Recall@20 lifts up to +32.6% with backbone-specific $(\gamma, \kappa)$ tuning and +1.9% to +16.0% for DualSpectralCF-Cheby at the fixed default $(\gamma = -0.5, \kappa = 0.1)$, and the family runs 7.7 to 155.3$\times$ faster than SIGformer while reaching 70.7% to 90.7% of its accuracy. Sign-awareness helps most for cold-start users, with up to +29.2% Recall@20 on Epinions users with 1 to 5 training items.
