Evaluating review helpfulness is pivotal in assessing the caliber of airline reviews, instigating lively debates in both academic and practical spheres. This study endeavors to construct a comprehensive conceptual framework grounded in signaling theory, recognizing two factors as indicators shaping the perceived helpfulness of reviews. Empirical analysis was conducted using 82,539 reviews from nine airlines on TripAdvisor. Initially, the study scrutinizes the combined impact of review sentiment and consumer rating, followed by exploring the influence of review inconsistency on review helpfulness. Our experimental results show that most variables achieved a significance of one thousandth. Additionally, we shed light on the moderating effects of several heuristic clues in the model, including text length, seat class, and region. These findings underscore those heuristic clues that collectively influence the helpfulness of reviews. The outcomes of this research can aid airlines in identifying the most helpful reviews, thereby mitigating consumer search costs and empowering reviewers to contribute more valuable insights.

An optimal representation should contain the maximum task-relevant information and minimum task-irrelevant information, as revealed from Information Bottleneck Principle. In video action recognition, CNN based approaches have obtained better spatio-temporal representation by modeling temporal context. However, these approaches still suffer low generalization. In this paper, we propose a moderate optimization based approach called Dual-view Temporal Regularization (DTR) based on Information Bottleneck Principle for an effective and generalized video representation without sacrificing any efficiency of the model. On the one hand, we design Dual-view Regularization (DR) to constrain task-irrelevant information, which can effectively compress background and irrelevant motion information. On the other hand, we design Temporal Regularization (TR) to maintain task-relevant information by finding an optimal difference between frames, which benefits extracting sufficient motion information. The experimental results demonstrate: (1) DTR is orthogonal to temporal modeling as well as data augmentation, and it achieves general improvement on both model-based and data-based approaches; (2) DTR is effective among 7 different datasets, especially on motion-centric datasets i.e. SSv1/ SSv2, in which DTR gets 6%/3.8% absolute gains in top-1 accuracy.

The instance discrimination paradigm has become dominant in unsupervised learning. It always adopts a teacher-student framework, in which the teacher provides embedded knowledge as a supervision signal for the student. The student learns meaningful representations by enforcing instance spatial consistency with the views from the teacher. However, the outputs of the teacher can vary dramatically on the same instance during different training stages, introducing unexpected noise and leading to catastrophic forgetting caused by inconsistent objectives. In this paper, we first integrate instance temporal consistency into current instance discrimination paradigms, and propose a novel and strong algorithm named Temporal Knowledge Consistency (TKC). Specifically, our TKC dynamically ensembles the knowledge of temporal teachers and adaptively selects useful information according to its importance to learning instance temporal consistency. Experimental result shows that TKC can learn better visual representations on both ResNet and AlexNet on linear evaluation protocol while transfer well to downstream tasks. All experiments suggest the good effectiveness and generalization of our method. Code will be made available.