用模糊集改进推荐系统中的乘积量化，提升召回率且保持低计算成本。
Fuzzy Norm-Explicit Product Quantization for Recommender Systems
- 引入二型模糊集构建码本，允许子向量关联多个码本元素
- 在Netflix、Audio、Cifar60k上召回率分别达94%、69%、59%，优于现有方法
- 计算复杂度与最优现有方法相当，适合高并发推荐场景
随着数据资源增长，提供精准推荐以应对信息过载成为商业与生活的重要需求。然而，如何高效生成相关推荐始终是挑战。乘积量化（PQ）因其低复杂度成为生成相关推荐的高效技术。其核心难点在于提升召回率的同时不增加计算开销，适用于需快速返回大量潜在相关项的场景，如在线购物推荐。本文提出一种基于范数的模糊乘积量化方法，采用二型模糊集（T2FS）定义码本，使子向量可关联多个码本元素，并通过积分计算实现范数处理。该方法显著提升召回性能，在Netflix、Audio、Cifar60k数据集上分别达到94%、69%、59%的召回率，较NEQ、PQ、RQ等方法分别提升+6%、+5%、+8%。同时，计算时间与复杂度接近当前最高效的PQ方法，兼具高效率与高召回优势。
原文摘要 · Abstract (English)
As the data resources grow, providing recommendations that best meet the demands has become a vital requirement in business and life to overcome the information overload problem. However, building a system suggesting relevant recommendations has always been a point of debate. One of the most cost-efficient techniques in terms of producing relevant recommendations at a low complexity is Product Quantization (PQ). PQ approaches have continued developing in recent years. This system's crucial challenge is improving product quantization performance in terms of recall measures without compromising its complexity. This makes the algorithm suitable for problems that require a greater number of potentially relevant items without disregarding others, at high-speed and low-cost to keep up with traffic. This is the case of online shops where the recommendations for the purpose are important, although customers can be susceptible to scoping other products. This research proposes a fuzzy approach to perform norm-based product quantization. Type-2 Fuzzy sets (T2FSs) define the codebook allowing sub-vectors (T2FSs) to be associated with more than one element of the codebook, and next, its norm calculus is resolved by means of integration. Our method finesses the recall measure up, making the algorithm suitable for problems that require querying at most possible potential relevant items without disregarding others. The proposed method outperforms all PQ approaches such as NEQ, PQ, and RQ up to +6%, +5%, and +8% by achieving a recall of 94%, 69%, 59% in Netflix, Audio, Cifar60k datasets, respectively. More and over, computing time and complexity nearly equals the most computationally efficient existing PQ method in the state-of-the-art.
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