The Effectiveness of a Smoking Cessation Intervention Program Based upon a Process Model of Health Motivation

The purpose of the present study was to investigate the effect of participation in a health motivation-based intervention program on college students’ smoking behavior. One hundred and seventy smokers (mean age = 19.0 years, 151 males) from nine colleges and universities in Chengdu, China were randomly assigned to one of 5 groups that received between one and four sessions of the intervention, or no intervention. The intervention sessions included sequential activities based on the stages of the process model of health motivation. Each group completed questionnaires assessing health motivation and smoking behaviors at pre-test, immediately post-intervention, and at one month follow-up. Analyses indicated that the intervention program did improve participants’ health motivation, and that was associated with reduced levels of smoking relative to baseline. The greater the number of sessions, the greater the reduction in smoking

A framework for mining lifestyle profiles through multi-dimensional and high-order mobility feature clustering

Human mobility demonstrates a high degree of regularity, which facilitates the discovery of lifestyle profiles. Existing research has yet to fully utilize the regularities embedded in high-order features extracted from human mobility records in such profiling. This study proposes a progressive feature extraction strategy that mines high-order mobility features from users' moving trajectory records from the spatial, temporal, and semantic dimensions. Specific features are extracted such as travel motifs, rhythms decomposed by discrete Fourier transform (DFT) of mobility time series, and vectorized place semantics by word2vec, respectively to the three dimensions, and they are further clustered to reveal the users' lifestyle characteristics. An experiment using a trajectory dataset of over 500k users in Shenzhen, China yields seven user clusters with different lifestyle profiles that can be well interpreted by common sense. The results suggest the possibility of fine-grained user profiling through cross-order trajectory feature engineering and clustering

Towards Open-World Product Attribute Mining: A Lightly-Supervised Approach

We present a new task setting for attribute mining on e-commerce products, serving as a practical solution to extract open-world attributes without extensive human intervention. Our supervision comes from a high-quality seed attribute set bootstrapped from existing resources, and we aim to expand the attribute vocabulary of existing seed types, and also to discover any new attribute types automatically. A new dataset is created to support our setting, and our approach Amacer is proposed specifically to tackle the limited supervision. Especially, given that no direct supervision is available for those unseen new attributes, our novel formulation exploits self-supervised heuristic and unsupervised latent attributes, which attains implicit semantic signals as additional supervision by leveraging product context. Experiments suggest that our approach surpasses various baselines by 12 F1, expanding attributes of existing types significantly by up to 12 times, and discovering values from 39% new types.Comment: Accepted to ACL 202