The collusive fraudulent behaviors on e-commerce platforms lead to proliferation of fraudulent reviews, which disrupt fair competition among merchants and mislead consumers’ shopping decisions. Detection of spammer groups helps purify the e-commerce environment and enhances consumers’ shopping experience. However, existing graph-based methods for detecting spammer groups first learn user node vector representations from the graph, and then use clustering methods to obtain candidate groups. Such separate two-stage detection methods are difficult to obtain high-quality candidate groups, resulting in suboptimal detection performance. Additionally, current graph construction methods used in spammer group detection do not fully consider the characteristics of spammer groups, which limits the detection performance. Aiming these concerns, we integrate heterogeneous graph attention network (HGAN) with label propagation (LP) for detecting spammer groups. First, we build a heterogeneous weighted directed (HWD) graph by analyzing the dataset and assign an initial label to each node. Then, we integrate a HGAN-module with an LP-module to obtain the HWD graph’s node embeddings and simultaneously generate candidate groups. We enhance the quality of embeddings and groups through the collaborative optimization between the predicted labels obtained from the HGAN-module and the pseudo-labels obtained from the LP-module. Finally, we calculate the suspiciousness values of groups using the reconstruction loss of the autoencoder for spammer group identification. Experiments conducted on real-world review datasets, including Amazon, Yelp, and YelpChi, demonstrate that our method achieves significant improvements in average Precision@k and Recall@k metrics compared with state-of-the-art baseline approaches.

Detecting collusive spammers who collaboratively post fake reviews is extremely important to guarantee the reliability of review information on e-commerce platforms. In this research, we formulate the collusive spammer detection as an anomaly detection problem and propose a novel detection approach based on heterogeneous graph attention network . First, we analyze the review dataset from different perspectives and use the statistical distribution to model each user's review behavior. By introducing the Bhattacharyya distance , we calculate the user-user and product-product correlation degrees to construct a multi-relation heterogeneous graph. Second, we combine the biased random walk strategy and multi-head self-attention mechanism to propose a model of heterogeneous graph attention network to learn the node embeddings from the multi-relation heterogeneous graph. Finally, we propose an improved community detection algorithm to acquire candidate spamming groups and employ an anomaly detection model based on the autoencoder to identify collusive spammers. Experiments show that the average improvements of precision@k and recall@k of the proposed approach over the best baseline method on the Amazon , Yelp_Miami, Yelp_New York, Yelp_San Francisco, and YelpChi datasets are [13%, 3%], [32%, 12%], [37%, 7%], [42%, 10%], and [18%, 1%], respectively.

Collaborative recommender systems are vulnerable to shilling attacks. To address this issue, many methods including supervised and unsupervised have been proposed. However, supervised detection methods require training classifiers and they only apply to detect known types of attacks. The existing unsupervised detection methods need to know the prior knowledge of attacks, otherwise they suffer from low detection precision. In this paper, we present BS-SC, an unsupervised approach for detecting shilling profiles, which does not need to know the attack size or to label the candidate spammers. BS-SC starts from an in-depth analysis of user behaviors and uses two key mechanisms (i.e., behavior features extraction and behavior similarity matrix clustering) to distinguish shilling profiles from genuine ones. The behavior features reflect the behavior difference between genuine and shilling profiles, and the behavior similarity matrix clustering is to cluster shilling profiles based on their highly similar behaviors. Experimental results on the MovieLens and the sampled Amazon review datasets indicate that BS-SC outperforms the baseline unsupervised approaches, even when the prior knowledge is given for them.

Personalized support for users becomes even more important, when service access takes place in open and dynamic service-oriented environment. This paper shows how to realize personalized service support in cross-system/service environments based on ontology and Web service technologies. First, we introduce the related approaches for supporting cross-system personalization and give their insufficiency respectively. Aimed at the problems we propose a Web service based architecture for cross-system personalization. The loosely coupled structure of Web service can easily integrate personalization service from various information systems and provide seamless access to the users. In order to reuse user models we also present an ontology based user model to support cross-system personalization. Compared with the existing approaches, our approach can effectively support the various existing personalized systems and user models, and the realization of cross-system personalization is more simply and efficiently