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Yuanbo Xu

Bio: Yuanbo Xu is an academic researcher from Jilin University. The author has contributed to research in topics: Computer science & Recommender system. The author has an hindex of 7, co-authored 24 publications receiving 144 citations.

Papers
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Journal ArticleDOI
TL;DR: A novel latent factor model called adaptive deep latentfactor model (ADLFM), which learns the preference factor of users adaptively in accordance with the specific items under consideration, and a novel user representation method that is derived from their rated item descriptions instead of original user-item ratings is proposed.
Abstract: In the existing recommender systems, matrix factorization (MF) is widely applied to model user preferences and item features by mapping the user-item ratings into a low-dimension latent vector space. However, MF has ignored the individual diversity where the user’s preference for different unrated items is usually different. A fixed representation of user preference factor extracted by MF cannot model the individual diversity well, which leads to a repeated and inaccurate recommendation. To this end, we propose a novel latent factor model called adaptive deep latent factor model (ADLFM), which learns the preference factor of users adaptively in accordance with the specific items under consideration. We propose a novel user representation method that is derived from their rated item descriptions instead of original user-item ratings. Based on this, we further propose a deep neural networks framework with an attention factor to learn the adaptive representations of users. Extensive experiments on Amazon data sets demonstrate that ADLFM outperforms the state-of-the-art baselines greatly. Also, further experiments show that the attention factor indeed makes a great contribution to our method.

43 citations

Journal ArticleDOI
TL;DR: A novel method Multiple Data Estimation (MDE) is proposed to estimate the congestion status in urban environment with GPS trajectory data efficiently, where it estimates the congestionstatus of the area through utilizing multiple properties, including density, velocity, inflow and previous status.

39 citations

Journal ArticleDOI
TL;DR: By preventing uninteresting and unsatisfying items to be recommended as top-N items, this concise-but-novel method improves accuracy and recommendation quality (especially serendipity) substantially and can be applied to most existing CF methods, such as item-based CF, user- based CF and matrix factorization-basedCF.
Abstract: In this paper, we study how to address the sparsity, accuracy and serendipity issues of top-N recommendation with collaborative filtering (CF). Existing studies commonly use rated items (which form only a small section in a rating matrix) or import some additional information (e.g., details about the items and users) to improve the performance of CF. Unlike these methods, we propose a novel notion towards a huge amount of unrated items: serendipity item. By utilizing serendipity items, we propose concise satisfaction and interest injection (CSII), a method that can effectively find interesting, satisfying, and serendipitous items in unrated items. By preventing uninteresting and unsatisfying items to be recommended as top-N items, this concise-but-novel method improves accuracy and recommendation quality (especially serendipity) substantially. Meanwhile, it can address the sparsity and cold-start issues by enriching the rating matrix in CF without additional information. As our method tackles rating matrix before recommendation procedure, it can be applied to most existing CF methods, such as item-based CF, user-based CF and matrix factorization-based CF. Through comprehensive experiments using abundant real-world datasets with LensKit implementation, we successfully demonstrate that our solution improves the performance of existing CF methods consistently and universally. Moreover, comparing with baseline methods, CSII can extract uninteresting items more carefully and cautiously, avoiding potential items inferred by mistake.

36 citations

Journal ArticleDOI
Yongjian Yang1, Yuanbo Xu1, En Wang1, Kaihao Lou1, Dongming Luan1 
TL;DR: This paper proposes an Information Maximization strategy in Online and Offline double-layer Propagation scheme (IMOOP), where the first form the topological graph for online social network and offline connection graph of probability, respectively, and the two layers are compressed into a single-layer communication graph.

25 citations

Journal ArticleDOI
TL;DR: Extensive experiments on five real-world datasets show significant improvements of DLFM-HSM over the state-of-the-art methods and demonstrate the effectiveness of the model for alleviating the data sparsity problem.

21 citations


Cited by
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Journal ArticleDOI
14 Jul 2020
TL;DR: In this paper, the authors provide a focused survey on KG-based recommender system via a holistic perspective of both technologies and applications, and present their opinions on the prospects of KG based recommender systems and suggest some future research directions.
Abstract: Recommender system (RS) targets at providing accurate item recommendations to users with respect to their preferences; it has been widely employed in various online applications for addressing the problem of information explosion and improving user experience. In the past decades, while tremendous efforts have been made in enhancing the performance of RSs, some long-standing challenges, such as data sparsity, cold start, and result diversity, are unaddressed. Along this line, an emerging research trend is to exploit the rich semantic information contained in the knowledge graph (KG); it has been proven to be an effective way to enhance the capability of RSs. To this end, we provide a focused survey on KG-based RS via a holistic perspective of both technologies and applications. Specifically, firstly, we briefly review the core concepts and classical algorithms of the RSs and KGs. Secondly, we comprehensively introduce the representative and state-of-the-art works in this field based on different strategies of exploiting KGs for RSs. Meanwhile, we also summarize some typical application scenarios of KG-based RSs, for facilitating the hands-on practices of corresponding algorithms. Finally, we present our opinions on the prospects of KG-based RS and suggest some future research directions in this area.

278 citations

Journal ArticleDOI
01 May 2020
TL;DR: This study comprehensively compile and summarize the existing fake reviews-related public datasets and proposes an antecedent–consequence–intervention conceptual framework to develop an initial research agenda for investigating fake reviews.
Abstract: Fake online reviews in e-commerce significantly affect online consumers, merchants, and, as a result, market efficiency. Despite scholarly efforts to examine fake reviews, there still lacks a survey that can systematically analyze and summarize its antecedents and consequences. This study proposes an antecedent–consequence–intervention conceptual framework to develop an initial research agenda for investigating fake reviews. Based on a review of the extant literature on this issue, we identify 20 future research questions and suggest 18 propositions. Notably, research on fake reviews is often limited by lack of high-quality datasets. To alleviate this problem, we comprehensively compile and summarize the existing fake reviews-related public datasets. We conclude by presenting the theoretical and practical implications of the current research.

156 citations

Journal ArticleDOI
TL;DR: A novel multimodal representation learning-based model (MRLM) that remarkably improved the recommendation effectiveness in IoT through extensive experiments on two real-world datasets.
Abstract: The recommender system has recently drawn a lot of attention to the communities of information services and mobile applications. Many deep learning-based recommendation models have been proposed to learn the feature representations from items. However, in Internet of Things (IoT), items’ description information are typically heterogeneous and multimodal, posing a challenge to items’ representation learning of recommendation models. To address this challenge and to improve the recommendation effectiveness in IoT, a novel multimodal representation learning-based model (MRLM) has been proposed. In MRLM, two closely related modules were trained simultaneously; they are global feature representation learning and multimodal feature representation learning. The former was designed to learn to accurately represent the global features of items and users through simultaneous training on three tasks: 1) triplet metric learning; 2) softmax classification; and 3) microscopic verification. The latter was proposed to refine items’ global features and to generate the final multimodal features by using items’ multimodal description information. After MRLM converged, items’ multimodal features and users’ global features could be used to calculate users’ preferences on items via cosine similarity. Through extensive experiments on two real-world datasets, MRLM remarkably improved the recommendation effectiveness in IoT.

132 citations

Journal ArticleDOI
TL;DR: A competitive diffusion model, namely Linear Threshold model with One Direction state Transition (LT1DT), is proposed for modeling competitive information propagation of two different types in the same network and a novel heuristic based on diffusion dynamics is proposed to solve the problem of minimizing rumor spread.

105 citations