The Impact of Online Third-party Product Reviews on Consumer Adoption of a New Product

Research has discovered that the third-party product review (TPPR) can influence consumer adoption of a new product. However, the specific influence mechanism needs further deep explorations. In this study, effects of different types of TPPR on consumer adoption of different new products were tested from the perspective of consumer knowledge. Based on two experimental designs, it is reported that the TPPR in the recommendation format and comparison format influence the intentions of new consumers to use really new products mostly. The TPPR in the description format and independent format influence the intentions of expert consumers to incremental new products. Moreover, the source of TPPR is vital. Consumers trust the TPPR in professional product evaluation websites more compared with public media. In other words, TPPR in professional product evaluation websites can facilitate consumer adoption of new products. The research conclusions here further enrich researches concerning TPPR and adoption of new productions

The Impact of Online Word-of-mouth and Negative Media Exposure on Consumer Habitual Skepticism: The Mediating Effect of Attribution

How did habitual skepticism come into being? In this research，the causes of consumer habitual skepticism are explored from the perspective of attribution. We put forward two important antecedent variables, negative online word-of-mouth and negative media exposure. The study results show that the higher the negative word-of-mouth perception is, the higher the stability and controllability of consumer attribution will be, and the higher the degree of consumer habitual skepticism will be. The higher the intensity of negative media exposure is, the higher the stability and controllability of consumer attribution will be, and the higher the degree of consumer habitual skepticism will be. We test this framework through two experiments. Study 1 investigates the influence of negative word-of-mouth spread and media exposure on consumer habitual skepticism. Study 2 investigates the effect of two independent variables on consumer habitual skepticism from an overall point of view and explore the mediation effect of attribution

Neural Document Expansion with User Feedback

This paper presents a neural document expansion approach (NeuDEF) that enriches document representations for neural ranking models. NeuDEF harvests expansion terms from queries which lead to clicks on the document and weights these expansion terms with learned attention. It is plugged into a standard neural ranker and learned end-to-end. Experiments on a commercial search log demonstrate that NeuDEF significantly improves the accuracy of state-of-the-art neural rankers and expansion methods on queries with different frequencies. Further studies show the contribution of click queries and learned expansion weights, as well as the influence of document popularity of NeuDEF's effectiveness.Comment: The 2019 ACM SIGIR International Conference on the Theory of Information Retrieva

Unsupervised Multi-view Pedestrian Detection

With the prosperity of the video surveillance, multiple cameras have been applied to accurately locate pedestrians in a specific area. However, previous methods rely on the human-labeled annotations in every video frame and camera view, leading to heavier burden than necessary camera calibration and synchronization. Therefore, we propose in this paper an Unsupervised Multi-view Pedestrian Detection approach (UMPD) to eliminate the need of annotations to learn a multi-view pedestrian detector via 2D-3D mapping. 1) Firstly, Semantic-aware Iterative Segmentation (SIS) is proposed to extract unsupervised representations of multi-view images, which are converted into 2D pedestrian masks as pseudo labels, via our proposed iterative PCA and zero-shot semantic classes from vision-language models. 2) Secondly, we propose Geometry-aware Volume-based Detector (GVD) to end-to-end encode multi-view 2D images into a 3D volume to predict voxel-wise density and color via 2D-to-3D geometric projection, trained by 3D-to-2D rendering losses with SIS pseudo labels. 3) Thirdly, for better detection results, i.e., the 3D density projected on Birds-Eye-View from GVD, we propose Vertical-aware BEV Regularization (VBR) to constraint them to be vertical like the natural pedestrian poses. Extensive experiments on popular multi-view pedestrian detection benchmarks Wildtrack, Terrace, and MultiviewX, show that our proposed UMPD approach, as the first fully-unsupervised method to our best knowledge, performs competitively to the previous state-of-the-art supervised techniques. Code will be available

Robust Classification with Convolutional Prototype Learning

Convolutional neural networks (CNNs) have been widely used for image classification. Despite its high accuracies, CNN has been shown to be easily fooled by some adversarial examples, indicating that CNN is not robust enough for pattern classification. In this paper, we argue that the lack of robustness for CNN is caused by the softmax layer, which is a totally discriminative model and based on the assumption of closed world (i.e., with a fixed number of categories). To improve the robustness, we propose a novel learning framework called convolutional prototype learning (CPL). The advantage of using prototypes is that it can well handle the open world recognition problem and therefore improve the robustness. Under the framework of CPL, we design multiple classification criteria to train the network. Moreover, a prototype loss (PL) is proposed as a regularization to improve the intra-class compactness of the feature representation, which can be viewed as a generative model based on the Gaussian assumption of different classes. Experiments on several datasets demonstrate that CPL can achieve comparable or even better results than traditional CNN, and from the robustness perspective, CPL shows great advantages for both the rejection and incremental category learning tasks