Xiuli Ma | Search arXiv

arXiv2026

Behavior2Value: Benchmarking and Empowering LLMs for Consumer Value Measurement from E-commerce Behaviors

Human values are deep motivational orientations that shape human behaviors. In e-commerce, they reveal the stable drivers behind users' purchase decisions. Compared with short-term interests, consumer values better explain how users evaluate products before purchase. However, consumer values are often implicit in complex and fragmented behavioral trajectories, leaving value measurement from e-commerce behaviors largely underexplored. To this end, we propose the Behavior-to-Value (B2V) task, which aims to identify consumer values from e-commerce behavioral trajectories. Centered on this task, we first construct the E-commerce Consumption Value Taxonomy (ECVT) and introduce B2V-Bench, the first B2V dataset and benchmark, based on anonymized Taobao behavioral logs. B2V-Bench consists of real-world purchase decision episodes, covering 25 types of purchase behaviors, along with corresponding consumer value orientations manifested in each episode. To improve consumer value measurement accuracy, we further present B2V-Verifier, a behavior-to-value measurement model based on Value Verification Tuning, which learns to assess whether behaviors provide sufficient evidence for each value inference. Experiments show that B2V-Verifier outperforms strong LLM baselines, improving multi-label classification by 34\%. The dataset and code will be publicly released upon acceptance.

cs.CL↗

arXiv2016

Complexes Detection in Biological Networks via Diversified Dense Subgraphs Mining

Protein-protein interaction (PPI) networks, providing a comprehensive landscape of protein interacting patterns, enable us to explore biological processes and cellular components at multiple resolutions. For a biological process, a number of proteins need to work together to perform the job. Proteins densely interact with each other, forming large molecular machines or cellular building blocks. Identification of such densely interconnected clusters or protein complexes from PPI networks enables us to obtain a better understanding of the hierarchy and organization of biological processes and cellular components. Most existing methods apply efficient graph clustering algorithms on PPI networks, often failing to detect possible densely connected subgraphs and overlapped subgraphs. Besides clustering-based methods, dense subgraph enumeration methods have also been used, which aim to find all densely connected protein sets. However, such methods are not practically tractable even on a small yeast PPI network, due to high computational complexity. In this paper, we introduce a novel approximate algorithm to efficiently enumerate putative protein complexes from biological networks. The key insight of our algorithm is that we do not need to enumerate all dense subgraphs. Instead we only need to find a small subset of subgraphs that cover as many proteins as possible. The problem is formulated as finding a diverse set of dense subgraphs, where we develop highly effective pruning techniques to guarantee efficiency. To handle large networks, we take a divide-and-conquer approach to speed up the algorithm in a distributed manner. By comparing with existing clustering and dense subgraph-based algorithms on several human and yeast PPI networks, we demonstrate that our method can detect more putative protein complexes and achieves better prediction accuracy.

q-bio.MN↗