With the development of the Internet and the application of cloud computing, the number of Web Services with similar functions is increasing. How to recommend high-quality services that satisfy their needs to users based on the quality of service (QoS) prediction results of Web Services is becoming increasingly important. Using the non-functional attributes of Web Services for QoS prediction has become a major research issue. Among the existing researches, methods based on collaborative filtering (CF) are firstly used in the field of QoS prediction. However, due to the sparsity of the call records of Web Services in the real world, traditional CF methods are greatly limited. In addition, the feature interaction of different dimensions and the importance of different feature combinations between the geographical location of users and services are rarely considered at the same time. In order to address the above, this paper proposes a new QoS prediction model that combines CF and feature interaction learning. The model innovatively uses similarity adaptive corrector to correct QoS, employs an attention factorization machine to model low-order feature interaction, and uses deep neural network to model high-order feature interaction. We conducted a comprehensive experiment with real-world datasets and achieved better QoS prediction accuracy.

Polarized Communities Search (PCS) aims to identify query-dependent communities where positive links predominantly connect nodes within each community, while negative links primarily connect nodes across different communities. Existing solutions primarily focus on modeling network topology, disregarding the crucial factor of node attributes. However, it is non-trivial to incorporate node attributes into PCS. In this paper, we propose a novel method called CO-guided RAndom walk in attributed signed networks (CORA) for PCS. Our approach involves constructing an attribute-based signed network to represent the auxiliary relations between nodes. We introduce a weight assignment mechanism to assess the reliability of edges in the signed network. Then, we design a co-guided random walk scheme that operates on two signed networks to model the connections between network topology and node attributes, thereby enhancing the search outcomes. Finally, we identify polarized communities using the Rayleigh quotient in the signed network. Extensive experiments conducted on three public datasets demonstrate the superior performance of CORA compared to state-of-the-art baselines for polarized communities search.

Polarized communities search aims at locating query-dependent communities, in which mostly nodes within each community form intensive positive connections, while mostly nodes across two communities are connected by negative links. Current approaches towards polarized communities search typically model the network topology, while the key factor of node, i.e., the attributes, are largely ignored. Existing studies have shown that community formation is strongly influenced by node attributes and the formation of communities are determined by both network topology and node attributes simultaneously. However, it is nontrivial to incorporate node attributes for polarized communities search. Firstly, it is hard to handle the heterogeneous information from node attributes. Secondly, it is difficult to model the complex relations between network topology and node attributes in identifying polarized communities. To address the above challenges, we propose a novel method Co-guided Random Walk in Attributed signed networks (CoRWA) for polarized communities search by equipping with reasonable attribute setting. For the first challenge, we devise an attribute-based signed network to model the auxiliary relation between nodes and a weight assignment mechanism is designed to measure the reliability of the edges in the signed network. As to the second challenge, a co-guided random walk scheme in two signed networks is designed to explicitly model the relations between topology-based signed network and attribute-based signed network so as to enhance the search result of each other. Finally, we can identify polarized communities by a well-designed Rayleigh quotient in the signed network. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed CoRWA. Further analysis reveals the significance of node attributes for polarized communities search.