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Diversified and scalable service recommendation with accuracy guarantee

- Wang, Lina, Zhang, Xuyun, Wang, Tian, Wan, Shaohua, Pang, Shaoning

  • Authors: Wang, Lina , Zhang, Xuyun , Wang, Tian , Wan, Shaohua , Pang, Shaoning
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Computational Social Systems Vol. 8, no. 5 (2021), p. 1182-1193
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  • Description: As one of the most successful recommendation techniques, neighborhood-based collaborative filtering (CF), which recommends appropriate items to a target user by identifying similar users or similar items, has been widely applied to various recommender systems. Although many neighbor-based CF methods have been put forward, there are still some open issues that have remained unsolved. First, the ever-increasing volume of user-item rating data decreases the recommendation efficiency significantly as a recommender system needs to analyze all the rating data when searching for similar neighbors or similar items. In this situation, users' requirements on quick response may not be met. Second, in neighbor-based CF methods, more attention is paid to the recommendation accuracy while other key indicators of recommendation performances are often ignored, i.e., recommendation diversity (RD), which probably produces similar or redundant items in the recommended list and decreases users' satisfaction. Considering these issues, a diversified and scalable recommendation method (called DR_LT) based on locality-sensitive hashing and cover tree is proposed in this article, where the item topic information is used to optimize the final recommended list. We show the effectiveness of our proposed method through a set of experiments on MovieLens data set that clearly shows the feasibility of our proposal in terms of item recommendation accuracy, diversity, and scalability. © 2014 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Shaoning Pang” is provided in this record**

Detecting outlier patterns with query-based artificially generated searching conditions

- Yu, Shuo, Xia, Feng, Sun, Yuchen, Tang, Tao, Yan, Xiaoran, Lee, Ivan

  • Authors: Yu, Shuo , Xia, Feng , Sun, Yuchen , Tang, Tao , Yan, Xiaoran , Lee, Ivan
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Computational Social Systems Vol. 8, no. 1 (2021), p. 134-147
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  • Description: In the age of social computing, finding interesting network patterns or motifs is significant and critical for various areas, such as decision intelligence, intrusion detection, medical diagnosis, social network analysis, fake news identification, and national security. However, subgraph matching remains a computationally challenging problem, let alone identifying special motifs among them. This is especially the case in large heterogeneous real-world networks. In this article, we propose an efficient solution for discovering and ranking human behavior patterns based on network motifs by exploring a user's query in an intelligent way. Our method takes advantage of the semantics provided by a user's query, which in turn provides the mathematical constraint that is crucial for faster detection. We propose an approach to generate query conditions based on the user's query. In particular, we use meta paths between the nodes to define target patterns as well as their similarities, leading to efficient motif discovery and ranking at the same time. The proposed method is examined in a real-world academic network using different similarity measures between the nodes. The experiment result demonstrates that our method can identify interesting motifs and is robust to the choice of similarity measures. © 2014 IEEE.

MODEL : motif-based deep feature learning for link prediction

- Wang, Lei, Ren, Jing, Xu, Bo, Li, Jianxin, Luo, Wei, Xia, Feng

  • Authors: Wang, Lei , Ren, Jing , Xu, Bo , Li, Jianxin , Luo, Wei , Xia, Feng
  • Date: 2020
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Computational Social Systems Vol. 7, no. 2 (2020), p. 503-516
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  • Description: Link prediction plays an important role in network analysis and applications. Recently, approaches for link prediction have evolved from traditional similarity-based algorithms into embedding-based algorithms. However, most existing approaches fail to exploit the fact that real-world networks are different from random networks. In particular, real-world networks are known to contain motifs, natural network building blocks reflecting the underlying network-generating processes. In this article, we propose a novel embedding algorithm that incorporates network motifs to capture higher order structures in the network. To evaluate its effectiveness for link prediction, experiments were conducted on three types of networks: social networks, biological networks, and academic networks. The results demonstrate that our algorithm outperforms both the traditional similarity-based algorithms (by 20%) and the state-of-the-art embedding-based algorithms (by 19%). © 2014 IEEE.
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