Product reviews and ratings on e-commerce websites provide customers with detailed insights about various aspects of the product such as quality, usefulness, etc. Since they influence customers' buying decisions, product reviews have become a fertile ground for abuse by sellers (colluding with reviewers) to promote their own products or tarnish the reputation of competitor's products. In this paper, our focus is on detecting such abusive entities (both sellers and reviewers) via a tensor decomposition on the product reviews data. While tensor decomposition is mostly unsupervised, we formulate our problem as a semi-supervised binary multi-target tensor decomposition, to take advantage of currently known abusive entities. We empirically show that our multi-target semi-supervised model achieves higher precision and recall in detecting abusive entities as compared with unsupervised techniques. Finally, we show that our proposed stochastic partial natural gradient inference for our model empirically achieves faster convergence than stochastic gradient and Online-EM with sufficient statistics.
There has been a lot of attention recently to address the issue of finding fake reviewers in online e-commerce platforms. @cite_9 were one of the first to show that review spam exists and proposed simple text based features to classify fake reviewers.
@cite_9
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Detecting review spam is important for current e-commerce applications. However, the posted order of review has been neglected by the former work. In this paper, we explore the issue on fake review detection in review sequence, which is crucial for implementing online anti-opinion spam. We analyze the characteristics of fake reviews firstly. Based on review contents and reviewer behaviors, six time sensitive features are proposed to highlight the fake reviews. And then, we devise supervised solutions and a threshold-based solution to spot the fake reviews as early as possible. The experimental results show that our methods can identify the fake reviews orderly with high precision and recall. Numerous reports have indicated the severity of fake reviews (i.e., spam) posted to various e-Commerce or opinion sharing Web sites. Nevertheless, very few studies have been conducted to examine the trustworthiness of online consumer reviews because of the lack of an effective computational methodology. Unlike other kinds of Web spam, untruthful reviews could just look like other legitimate reviews (i.e., ham), and so it is difficult to apply any features to distinguish the two classes. One main contribution of our research work is the development of a novel computational methodology to combat online review spam. Our experimental results confirm that the KL divergence and the probabilistic language modeling based computational model is effective for the detection of untruthful reviews. Empowered by the proposed computational methods, our empirical study found that around 2 of the consumer reviews posted to a large e-Commerce site is spam. The services and products of E-Commerce portals in this digital age are heavily reviewed by the users. These reviews provide useful insights on the quality usage of these products. Due to such importance of reviews, they can be faked to give false opinions about products and subsequently mislead the users. In this paper we are proposing new set of lexical and syntactic features set and applying supervised algorithms for performing classification on fake reviews dataset (gold standard). We focus on the writing style, that include type of punctuation mark, Part-of- Speech (POS) etc., that are helpful for detection of reviews spam. The final results give promising accuracy 91.51 for detecting fake reviews. Online reviews have become an increasingly important resource for decision making and product designing. But reviews systems are often targeted by opinion spamming. Although fake review detection has been studied by researchers for years using supervised learning, ground truth of large scale datasets is still unavailable and most of existing approaches of supervised learning are based on pseudo fake reviews rather than real fake reviews. Working with Dianping, the largest Chinese review hosting site, we present the first reported work on fake review detection in Chinese with filtered reviews from Dianping's fake review detection system. Dianping's algorithm has a very high precision, but the recall is hard to know. This means that all fake reviews detected by the system are almost certainly fake but the remaining reviews (unknown set) may not be all genuine. Since the unknown set may contain many fake reviews, it is more appropriate to treat it as an unlabeled set. This calls for the model of learning from positive and unlabeled examples (PU learning). By leveraging the intricate dependencies among reviews, users and IP addresses, we first propose a collective classification algorithm called Multi-typed Heterogeneous Collective Classification (MHCC) and then extend it to Collective Positive and Unlabeled learning (CPU). Our experiments are conducted on real-life reviews of 500 restaurants in Shanghai, China. Results show that our proposed models can markedly improve the F1 scores of strong baselines in both PU and non-PU learning settings. Since our models only use language independent features, they can be easily generalized to other languages.