Your search for: 'author:("Huang, Jiajin")' has returned 4 results. (0.014s) Save search

Authors: Wang, Jian | Huang, Jiajin | Zhong, Ning

Article Type: Research Article

Abstract: Recommender systems aim to provide users with preferred items to address the information overload problem in the Web era. Social relations, item connections, and user-generated item reviews and ratings play important roles in recommender systems as they contain abundant potential information. Many methods have been proposed to predict users’ ratings by learning latent topic factors from their reviews and ratings of corresponding items. However, these methods ignore the relationships among items and cannot make full use of the complicated relations between reviews and ratings. Motivated by this observation, we integrate ratings, reviews, user connections and item relations to improve recommendations …by combining matrix factorization with the Latent Dirichlet Allocation (LDA) model. Experimental results on two real-world datasets prove that item–item relations contain useful information for recommendations, and our model effectively improves recommendation quality. Show more

Keywords: Review-based recommender system, rating prediction, latent Dirichlet allocation (LDA) model

DOI: 10.3233/WEB-180370

Citation: Web Intelligence, vol. 16, no. 1, pp. 1-13, 2018

Authors: Yi, Jin | Huang, Jiajin | Qin, Jin

Article Type: Research Article

Abstract: Recommender systems have been widely used in our life in recent years to facilitate our life. And it is very important and meaningful to improve recommendation performance. Generally, recommendation methods use users’ historical ratings on items to predict ratings on their unrated items to make recommendations. However, with the increase of the number of users and items, the degree of data sparsity increases, and the quality of recommendations decreases sharply. In order to solve the sparsity problem, other auxiliary information is combined to mine users’ preferences for higher recommendation quality. Similar to rating data, review data also contain rich information …about users’ preferences on items. This paper proposes a novel recommendation model, which harnesses an adversarial learning among auto-encoders to improve recommendation quality by minimizing the gap of the rating and review relation between a user and an item. The empirical studies on real-world datasets show that the proposed method improves the recommendation performance. Show more

Keywords: Review, recommendation, adversarial learning, auto-encoder

DOI: 10.3233/WEB-210449

Citation: Web Intelligence, vol. 18, no. 4, pp. 285-294, 2020

Authors: Xu, Jianan | Huang, Jiajin | Yang, Jian | Zhong, Ning

Article Type: Research Article

Abstract: Graph Neural Networks (GNNs) have been successfully used to learn user and item representations for Collaborative Filtering (CF) based recommendations (GNN-CF). Besides the main recommendation task in a GNN-CF model, contrastive learning is taken as an auxiliary task to learn better representations. Both the main task and the auxiliary task face the noise problem and the distilling hard negative problem. However, existing GNN-CF models only focus on one of them and ignore the other. Aiming to solve the two problems in a unified framework, we propose a M ulti-M ixing strategy for G NN-based CF (M2GCF). In the main task, …M2GCF perturbs embeddings of users, items and negative items with sample-noise by a mixing strategy. In the auxiliary task, M2GCF utilizes a contrastive learning mechanism with a two-step mixing strategy to construct hard negatives. Extensive experiments on three benchmark datasets demonstrate the effectiveness of the proposed model. Further experimental analysis shows that M2GCF is robust against interaction noise and is accurate for long-tail item recommendations. Show more

Keywords: Recommender systems, collaborative filtering, graph neural networks, contrastive learning, mixing strategy

DOI: 10.3233/WEB-220054

Citation: Web Intelligence, vol. 21, no. 2, pp. 149-166, 2023

Authors: Chen, Bin | Yu, Li | Luo, Weiyi | Wu, Chizhong | Li, Manyu | Tan, Hai | Huang, Jiajin | Wan, Zhijiang

Article Type: Research Article

Abstract: Localizing the root cause of network faults is crucial to network operation and maintenance. Operational expenses will be saved if the root cause can be identified accurately. However, due to the complicated wireless environments and network architectures, accurate root cause localization of network falut meets the difficulties including missing data, hybrid fault behaviors, and short of well-labeled data. In this study, global and local features are constructed to make new feature representation for data sample, which can highlight the temporal characteristics and contextual information of the root cause analysis data. A hybrid tree model (HTM) ensembled by CatBoost, XGBoost and LightGBM …is proposed to interpret the hybrid fault behaviors from several perspectives and discriminate different root causes. Based on the combination of global and local features, a semi-supervised training strategy is utilized to train the HTM for dealing with short of well-labeled data. The experiments are conducted on the real-world dataset from ICASSP 2022 AIOps Challenge, and the results show that the global and local feature based HTM achieves the best model performance comparing with other models. Meanwhile, our solution achieves third place in the competition leaderboard which shows the model effectiveness. Show more

Keywords: Global feature, local feature, hybrid tree model, root cause analysis, network fault localization

DOI: 10.3233/WEB-220016

Citation: Web Intelligence, vol. 20, no. 3, pp. 213-223, 2022