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摘要 ：
Recommendation system has attracted large amount of attention in the field of E-commerce research. Traditional MF (Matrix Factorization) methods take a global view on the user-item rating matrix to derive latent user vectors and latent item vectors for rating prediction. However, there is an inherent structure in the user-item rating matrix and a local correspondence between user clusters and item clusters as the users induce the items and the items imply the users in a recommendation system. Motivated by this observation, this paper proposes a novel rating prediction approach called RP-LGMC (Rating Prediction based on Local and Global information with Matrix Clustering) based on matrix factorization by making use of the local correspondence between user clusters and item clusters. The RP-LGMC approach consists of three components. The first component is to partition the user-item rating matrix into small blocks by the sparse subspace clustering (SCC) algorithm with co-clustering its rows (users) and columns (items) simultaneously. The second component is local distillation to extract those dense and stable blocks by thresholding block density and standard deviation. The third component is to predict the ratings with residual approximation on the local blocks and SVD++ on the global blocks of the original user-item matrixR. The RP-LGMC approach can not only reduce the data sparsity but also increase the computation scalability. Experiments on the MovieLens-25 M dataset demonstrate that the proposed RP-LGMC approach performs better than most state-of-the-art methods in terms of recommendation accuracy and has lower computation complexity than the SVD++ algorithm. (C) 2021 Published by Elsevier Ltd.
关键词 ：
Global information Global information Local information Local information Rating prediction Rating prediction Residual approximation Residual approximation Sparse subspace clustering Sparse subspace clustering
引用：
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| GB/T 7714 | Zhang, Wen , Wang, Qiang , Yoshida, Taketoshi et al. RP-LGMC: Rating prediction based on local and global information with matrix clustering [J]. | COMPUTERS & OPERATIONS RESEARCH , 2021 , 129 . |
| MLA | Zhang, Wen et al. "RP-LGMC: Rating prediction based on local and global information with matrix clustering" . | COMPUTERS & OPERATIONS RESEARCH 129 (2021) . |
| APA | Zhang, Wen , Wang, Qiang , Yoshida, Taketoshi , Li, Jian . RP-LGMC: Rating prediction based on local and global information with matrix clustering . | COMPUTERS & OPERATIONS RESEARCH , 2021 , 129 . |
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摘要 ：
The development of Internet comes up with the prosperity of E-commerce all over the world. In order to promote sales and save consumers' labor in commodity browsing, recommender systems are proposed by E-commerce platforms to provide online consumers with products and services of their potential interests. The primary challenge in recommendation roots in the intricacy in quantifying users' preferences on items with the reality of data sparsity and the computation complexity. Hence, more and more researchers are attempting deep learning techniques to deal with the challenge with the hope of using advanced algorithms to alleviate the intricacy. Word embedding is used to learn the association of items in a space of low dimensionality. Multi-layer perception is used to learn users' preferences on items in a data-driven manner with a customized loss function. The future work of recommender systems includes three folds. The one is to make use of multi-source data to combine implicit and explicit user behavior data to address the problem of data sparsity. The second is dynamic recommendation with the changing users' preferences on items and make recommender systems light-weight and useable in complex scenarios. The third is to provide effective and verifiable recommendation under the premise of user privacy protection
关键词 ：
Data sparsity Data sparsity Deep learning Deep learning Privacy protection Privacy protection Recommendation algorithm Recommendation algorithm Recommender systems Recommender systems User preference User preference
引用：
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| GB/T 7714 | Zhang, Wen , Wang, Qiang , Yang, Ye et al. A 2020 perspective on "DeRec: A data-driven approach to accurate recommendation with deep learning and weighted loss function" [J]. | ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS , 2021 , 48 . |
| MLA | Zhang, Wen et al. "A 2020 perspective on "DeRec: A data-driven approach to accurate recommendation with deep learning and weighted loss function"" . | ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS 48 (2021) . |
| APA | Zhang, Wen , Wang, Qiang , Yang, Ye , Yoshida, Taketoshi . A 2020 perspective on "DeRec: A data-driven approach to accurate recommendation with deep learning and weighted loss function" . | ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS , 2021 , 48 . |
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摘要 ：
In this paper, we explore how a monopoly manufacturer chooses the market strategies and decides the optimal price to obtain maximum profit. We divide the market into the C-type market and the N-type market, and analyze the profitability of the monopoly manufacturer who takes the pure-selling, pure-leasing and hybrid strategy respectively, considering consumers' capital constraint and the life span of the durable goods in an indefinite time horizon model. (1) We find that a larger proportion of the consumers with capital constraint has a more significant impact on the prices and it could slow down the development of the rental market. When the scale of the group attains up to a threshold level, it would greatly influence the customers' demand and their marketing strategy, and encourages more manufacturers to take the leasing strategy. (2) In the Hybrid Strategy, we see explicit growth in the overall profit with both the leasing channel and the selling channel working together, although the former outperforms the latter. The suppressed selling channel, in fact, has to lower the price to keep the market coverage, with an independent market structure. (3) Finally, we find that a leasing agent may help the manufacturer at first and then become a tough competitor. These findings provide new insights for the operation of large construction machinery manufacturing companies. subject classification numbers as needed.
关键词 ：
Capital constraint Capital constraint Durable goods Durable goods Leasing channel Leasing channel Selling channel Selling channel
引用：
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| GB/T 7714 | Li, Jian , Wang, Huan , Deng, Zhiwen et al. Leasing or selling? The channel choice of durable goods manufacturer considering consumers' capital constraint [J]. | FLEXIBLE SERVICES AND MANUFACTURING JOURNAL , 2021 . |
| MLA | Li, Jian et al. "Leasing or selling? The channel choice of durable goods manufacturer considering consumers' capital constraint" . | FLEXIBLE SERVICES AND MANUFACTURING JOURNAL (2021) . |
| APA | Li, Jian , Wang, Huan , Deng, Zhiwen , Zhang, Wen , Zhang, Guoqing . Leasing or selling? The channel choice of durable goods manufacturer considering consumers' capital constraint . | FLEXIBLE SERVICES AND MANUFACTURING JOURNAL , 2021 . |
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摘要 ：
Traditional matrix factorization (MF) methods take a global view on the user-item rating matrix to conduct matrix decomposition for rating approximation. However, there is an inherent structure in the user-item rating matrix and a local correspondence between user clusters and item clusters as the users induce the items and the items imply the users in a recommendation system. This article proposes a novel approach called two-stage rating prediction (TS-RP) to matrix clustering with implicit information. In the first stage, implicit feedback is used to discover the inherent structure of the user-item rating matrix by spectral clustering. In the second stage, we conduct rating prediction on the dense blocks of explicit information of user-item clusters discovered in the first stage. The proposed TS-RP approach can not only alleviate the data sparsity problem in recommendation but also increase the computation scalability. Experiments on the MovieLens-100K data set demonstrate that the proposed TS-RP approach performs better than most state-of-the-art methods of rating prediction based on MF in terms of recommendation accuracy and computation complexity. © 2014 IEEE.
关键词 ：
Clustering algorithms Clustering algorithms Factorization Factorization Forecasting Forecasting Matrix algebra Matrix algebra
引用：
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| GB/T 7714 | Zhang, Wen , Li, Xiang , Li, Jian et al. A Two-Stage Rating Prediction Approach Based on Matrix Clustering on Implicit Information [J]. | IEEE Transactions on Computational Social Systems , 2020 , 7 (2) : 517-535 . |
| MLA | Zhang, Wen et al. "A Two-Stage Rating Prediction Approach Based on Matrix Clustering on Implicit Information" . | IEEE Transactions on Computational Social Systems 7 . 2 (2020) : 517-535 . |
| APA | Zhang, Wen , Li, Xiang , Li, Jian , Yang, Ye , Yoshida, Taketoshi . A Two-Stage Rating Prediction Approach Based on Matrix Clustering on Implicit Information . | IEEE Transactions on Computational Social Systems , 2020 , 7 (2) , 517-535 . |
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摘要 ：
Due to the anonymous and free-for-all characteristics of online forums, it is very hard for human beings to differentiate deceptive reviews from truthful reviews. This paper proposes a deep learning approach for text representation called DC Word (Deep Context representation by Word vectors) to deceptive review identification. The basic idea is that since deceptive reviews and truthful reviews are composed by writers without and with real experience on using the online purchased goods or services, there should be different contextual information of words between them. Unlike state-of-the-art techniques in seeking best linguistic features for representation, we use word vectors to characterize contextual information of words in deceptive and truthful reviews automatically. The average-pooling strategy (called DC Word-A) and max-pooling strategy (called DC Word-M) are used to produce review vectors from word vectors. Experimental results on the Spam dataset and the Deception dataset demonstrate that the DCWord-M representation with LR (Logistic Regression) produces the best performances and outperforms state-of-the-art techniques on deceptive reviewidentification. Moreover, the DC Word-M strategy outperforms the DC Word-A strategy in review representation for deceptive review identification. The outcome of this study provides potential implications for online review management and business intelligence of deceptive review identification.
关键词 ：
DC Word representation DC Word representation deceptive review identification deceptive review identification deep learning deep learning Online business intelligence Online business intelligence skip-gram model skip-gram model
引用：
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| GB/T 7714 | Zhang, Wen , Wang, Qiang , Li, Xiangjun et al. DCWord: A Novel Deep Learning Approach to Deceptive Review Identification by Word Vectors [J]. | JOURNAL OF SYSTEMS SCIENCE AND SYSTEMS ENGINEERING , 2019 , 28 (6) : 731-746 . |
| MLA | Zhang, Wen et al. "DCWord: A Novel Deep Learning Approach to Deceptive Review Identification by Word Vectors" . | JOURNAL OF SYSTEMS SCIENCE AND SYSTEMS ENGINEERING 28 . 6 (2019) : 731-746 . |
| APA | Zhang, Wen , Wang, Qiang , Li, Xiangjun , Yoshida, Taketoshi , Li, Jian . DCWord: A Novel Deep Learning Approach to Deceptive Review Identification by Word Vectors . | JOURNAL OF SYSTEMS SCIENCE AND SYSTEMS ENGINEERING , 2019 , 28 (6) , 731-746 . |
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摘要 ：
Traditional collaborative filtering techniques suffer from the data sparsity problem in practice. That is, only a small proportion of all items in the recommender system occur in a user's rated item list. However, in order to retrieve items meeting a user's interest, all possible candidate items should be investigated. To address this problem, this paper proposes a recommendation approach called DeepRec, based on feedforward deep neural network learning with item embedding and weighted loss function. Specifically, item embedding learns numerical vectors for item representation, and weighted loss function balances popularity and novelty of recommended items. Moreover, it introduces two strategies, i.e. sampling by random (Ran-Strategy) and sampling by distribution (Pro-Strategy), to leave one item as output and the remaining as input from each user's historically rated item list. Max-pooling and average-pooling are employed to combine individual item vectors to derive users' input vectors for feedforward deep neural network learning. Experiments on the App dataset and the Last.fm dataset demonstrate that the proposed DeepRec approach is superior to state-of-the-art techniques in recommending Apps and songs in terms of accuracy and diversity as well as complexity. (C) 2018 Elsevier Inc. All rights reserved.
关键词 ：
Deep neural network Deep neural network DeepRec DeepRec Item embedding Item embedding Recommender system Recommender system Weighted loss function Weighted loss function
引用：
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| GB/T 7714 | Zhang, Wen , Du, Yuhang , Yoshida, Taketoshi et al. DeepRec: A deep neural network approach to recommendation with item embedding and weighted loss function [J]. | INFORMATION SCIENCES , 2019 , 470 : 121-140 . |
| MLA | Zhang, Wen et al. "DeepRec: A deep neural network approach to recommendation with item embedding and weighted loss function" . | INFORMATION SCIENCES 470 (2019) : 121-140 . |
| APA | Zhang, Wen , Du, Yuhang , Yoshida, Taketoshi , Yang, Ye . DeepRec: A deep neural network approach to recommendation with item embedding and weighted loss function . | INFORMATION SCIENCES , 2019 , 470 , 121-140 . |
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摘要 ：
间歇曝气模式下短程硝化厌氧氨氧化同时除磷的一体化生物处理工艺，属于污水生物处理技术领域。该工艺在短程硝化厌氧氨氧化同时除磷的一体化反应器中实现的。一体化反应器内主要存在三种微生物菌群：以絮体形式存在的氨氧化菌(AOB)和聚磷菌(PAOs)及以颗粒形式存在的厌氧氨氧化菌。城市生活污水未经脱碳预处理直接进入一体化反应器中，通过间歇曝气的运行模式，有效抑制亚硝酸盐氧化菌的活性，并且能够在短程硝化厌氧氨氧化自养脱氮的过程中为强化生物除磷提供碳源和电子供体，实现零外加碳源的投加。短程硝化厌氧氨氧化同时除磷的一体化反应器在排水过程中能够通过筛分的方法，有效持留厌氧氨氧化菌颗粒，同时将细小的富含磷酸盐的絮体污泥淘洗出去。
引用：
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| GB/T 7714 | 彭永臻 , 张文 , 苗圆圆 et al. 间歇曝气模式下短程硝化厌氧氨氧化同时除磷的一体化生物处理工艺 : CN201810203285.7[P]. | 2018-03-13 . |
| MLA | 彭永臻 et al. "间歇曝气模式下短程硝化厌氧氨氧化同时除磷的一体化生物处理工艺" : CN201810203285.7. | 2018-03-13 . |
| APA | 彭永臻 , 张文 , 苗圆圆 , 王思萌 , 李夕耀 . 间歇曝气模式下短程硝化厌氧氨氧化同时除磷的一体化生物处理工艺 : CN201810203285.7. | 2018-03-13 . |
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摘要 ：
Traditional recommendation techniques present various methods to measure similarity of users and items to characterize the preferences. However, different similarity measure focus on different aspects of user-item rating list and, this may cause incomplete information leveraged by similarity measure in users' preference characterization leading to low accuracy on recommendation. This paper proposes a deep learning approach, i.e. DeRec, to learn the latent item association from user-item rating list directly for predictive recommendation without employing a similarity measure. The loss of each item is weighted by its historical probability rated by users' past preferences, in which a deep learning neural network is adopted to predict a user's potential interest on the items using the user's historical items as input. We also develop two strategies to produce input vectors and output vectors as sampling by random (Ran-Strategy) and sampling by distribution (Pro-Strategy) to train the deep neural network with considering the sequential characteristics of items rated by users. Experiments on the App dataset and the MovieLens dataset demonstrate that the proposed DeRec approach outperforms traditional collaborative filtering methods in recommending Apps and movies in both MAP and MRR measures.
关键词 ：
Data-driven approach Data-driven approach Deep neural network Deep neural network DeRec DeRec Recommender system Recommender system Weighted loss function Weighted loss function
引用：
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| GB/T 7714 | Zhang, Wen , Du, Yuhang , Yang, Ye et al. DeRec: A data-driven approach to accurate recommendation with deep learning and weighted loss function [J]. | ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS , 2018 , 31 : 12-23 . |
| MLA | Zhang, Wen et al. "DeRec: A data-driven approach to accurate recommendation with deep learning and weighted loss function" . | ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS 31 (2018) : 12-23 . |
| APA | Zhang, Wen , Du, Yuhang , Yang, Ye , Yoshida, Taketoshi . DeRec: A data-driven approach to accurate recommendation with deep learning and weighted loss function . | ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS , 2018 , 31 , 12-23 . |
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