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A Novel Learning Rate Function and Its Application on the SVD plus plus recommendation algorithm

IEEE ACCESS 2020年 8卷 14112-14122页

作者： Jiao, Jiangli Zhang, Xueying Li, Fenglian Wang, Yan Taiyuan Univ Technol Coll Informat & Comp Taiyuan 030024 Peoples R China

The recommendation algorithm based on Singular Value Decomposition (SVD)++ is a widely used algorithm for its good prediction performance. However, with the rapid increase of data in smart societies, the poor computational performance of the SVD++ recommendation algorithm becomes a prominent disadvantage, for it takes a longer time to optimize the objective function during constructing the prediction model. The learning rate function is a significant factor in the prediction model based on the SVD++ recommendation algorithm. It can directly affect the convergence speed of the prediction model and the performance of the model. The traditional model uses an exponential function, natural exponential function or piecewise constant as its learning rate function. In this paper, a novel adaptive learning rate (ALR) function is proposed, which combines the exponential with linear functions, and the function is applied to the SVD++ recommendation algorithm. The highlights of the paper are as follows. First, with a larger initial value, the proposed function descends quicker and tends to the end with a less step. Second, the theoretical properties of the proposed learning rate function are verified through theoretical analysis, including the theoretical proof of its convergence and the iteration speed comparison. Compared to the existing learning rate functions, the proposed ALR function works better on the convergence speed through mathematical derivation. Finally, the novel ALR function is applied to the SVD++ recommendation algorithm as recommendation model ALRSVD++. Some existing learning rate methods are used as benchmarks for illustrating the computation and prediction performances of proposed ALR function and its ALRSVD++ model. Experimental results demonstrated that the SVD++ recommendation algorithm based on the proposed ALR function improved computational efficiency of the training model ALRSVD++ significantly. Especially, to the larger size training dataset, the it

关键词： Computational efficiency learning rate function recommendation algorithm singular value decomposition (SVD)

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The dynamic competitive recommendation algorithm in social network services

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INFORMATION SCIENCES 2012年第1期187卷 1-14页

作者： Yu, Seok Jong Sookmyung Womens Univ Dept Comp Sci Seoul 140742 South Korea

As the number of Twitter users exceeds 175 million and the scale of social network increases, it is facing with a challenge to how to help people find right people and information conveniently. For this purpose, current social network services are adopting personalized recommender systems. Existing recommendation algorithms largely depend on one of content-based algorithm, collaborative filtering, or influential ranking analysis. However, these algorithms tend to suffer from the performance fluctuation phenomenon in common whenever an active user changes, and it is due to the diversities of personal characteristics such as the local social graph size, the number of followers, or sparsity of profile content. To overcome this limitation and to provide consistent and stable recommendation in social networks, this study proposes the dynamic competitive recommendation algorithm based on the competition of multiple component algorithms. This study shows that it outperforms previous approaches through performance evaluation on actual Twitter dataset. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Social network service Recommender system recommendation algorithm Twitter PageRank

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Dynamic network intelligent hybrid recommendation algorithm and its application in online shopping platform

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第5期40卷 9173-9185页

作者： Gao, Yuxuan Liang, Haiming Sun, Bingzhen Xidian Univ Sch Marxism Xian Shaanxi Peoples R China Sichuan Univ Business Sch Chengdu Sichuan Peoples R China Xidian Univ Sch Econ & Management Xian Shaanxi Peoples R China

With the rapid development of e-commerce, whether network intelligent recommendation can attract customers has become a measure of customer retention on online shopping platforms. In the literature about network intelligent recommendation, there are few studies that consider the difference preference of customers in different time periods. This paper proposes the dynamic network intelligent hybrid recommendation algorithm distinguishing time periods (DIHR), it is a integrated novel model combined with the DEMATEL and TOPSIS method to solved the problem of network intelligent recommendation considering time periods. The proposed method makes use of the DEMATEL method for evaluating the preference relationship of customers for indexes of merchandises, and adopt the TOPSIS method combined with intuitionistic fuzzy number (IFN) for assessing and ranking the merchandises according to the indexes. We specifically introduce the calculation steps of the proposed method, and then calculate its application in the online shopping platform.

关键词： DIHR recommendation algorithm network intelligent recommendation system online shopping platform

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Collaborative filtering recommendation algorithm integrating time windows and rating predictions

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APPLIED INTELLIGENCE 2019年第8期49卷 3146-3157页

作者： Zhang, Pengfei Zhang, Zhijun Tian, Tian Wang, Yigui Shandong Jianzhu Univ Sch Comp Sci & Technol Jinan 250001 Shandong Peoples R China

This paper describes a new collaborative filtering recommendation algorithm based on probability matrix factorization. The proposed algorithm decomposes the rating matrix into two nonnegative matrixes using a predictive rating model. After normalization processing, these two nonnegative matrixes provide useful probability semantics. The posterior distribution of the real part of the probability model is calculated by the variational inference method. Finally, the preferences for items that users have not rated can be predicted. The user-item rating matrix is supplemented by a preference prediction value, resulting in a dense rating matrix. Finally, time weighting is integrated into the rating matrix to construct the 3D user-item-time model, which gives the recommendation results. According to experiments using open Netflix, MovieLens, and Epinion datasets, the proposed algorithm is superior to several existing recommendation algorithms in terms of rating predictions and recommendation effects.

关键词： Probability matrix factorization Time weighting Probability model recommendation algorithm

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Accurate Item recommendation algorithm of itemrank based on tag and context information

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COMPUTER COMMUNICATIONS 2021年 176卷 282-289页

作者： Huang, Zhiliang Ma, Hong Wang, Shizhi Shen, Yuan Air Force Early Warning Acad Wuhan 430000 Peoples R China Huazhong Univ Sci & Technol Wuhan 430000 Peoples R China

The traditional itemrank recommendation algorithm only uses the two-dimensional relationship between user and item to achieve recommendation, without considering the important information (such as label information and context information) that plays an important role in the association between user and item, so the accuracy of recommendation needs to be improved. Therefore, in this paper, tag information and context information are integrated into the link relationship between user and item, and a user-context-item tag association graph is constructed. An itemrank recommendation algorithm is proposed by integrating tag and context information. Firstly, ap-ml-rbf-relm model is used to determine user labels, and then deep neural network is used to determine the relationship between tags. Finally, item and label are calculated The association weight between the signatures is used to implement the recommendation service for the target users. Experimental results on public datasets show that the proposed algorithm is better than the traditional recommendation algorithm in recommendation accuracy.

关键词： Tag Context ItemRank recommendation algorithm

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Dual Auto-Encoder Based Rating Prediction recommendation algorithm

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IEEE ACCESS 2022年 10卷 97289-97297页

作者： Xin, Gaowei Qin, Jiwei Song, Xiaoyuan Zheng, Jiong Xinjiang Univ Sch Informat Sci & Engn Urumqi 830046 Peoples R China Xinjiang Univ Key Lab Signal Detect & Proc Urumqi 830046 Xinjiang Uygur Peoples R China

Collaborative filtering is the most widely used method in recommendation algorithms, but it still faces the serious problem of data sparsity. Traditional collaborative filtering uses matrix decomposition to learn the latent features of users and items. As an extension model of matrix decomposition, Funk-SVD model has attracted wide attention due to its good scalability and easy implementation, but it is difficult to extract the latent features of users and items from sparse rating information because it essentially learns the linear relationship between users and items. To solve this problem, we propose a Dual auto-encoder based Rating Prediction recommendation algorithm (DRPRA) model. The DRPRA model uses the strong ability of deep learning in feature learning, which combines double auto-encoders with Funk-SVD. First, the auto-encoder captures the latent features of users and items respectively. Then, the Funk-SVD combines the user features with item features to reconstruct the rating matrix. After that, we minimize the error between original rating matrix and reconstructed rating matrix, and to alleviate the problem of data sparsity and improve the accuracy of rating prediction effectively. We conducted extensive experiments on Movielens-100K, Movie Tweeting-10k, and Film Trust datasets, and the results show that the rating prediction model based on dual auto-encoders has a superior recommendation performance.

关键词： Feature extraction Predictive models Prediction algorithms Representation learning Matrix decomposition Deep learning Data models recommendation algorithm auto-encoder Funk-SVD sparsity

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An item orientated recommendation algorithm from the multi-view perspective

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NEUROCOMPUTING 2017年 269卷 261-272页

作者： Hu, Qi-Ying Zhao, Zhi-Lin Wang, Chang-Dong Lai, Jian-Huang Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Guangdong Peoples R China Guangdong Key Lab Informat Secur Technol Guangzhou 510006 Guangdong Peoples R China Sun Yat Sen Univ Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou 510006 Guangdong Peoples R China

In the traditional recommendation algorithms, items are recommended to users on the basis of users' preferences to improve selling efficiency, which however cannot always raise revenues for manufacturers of particular items. Assume that, a manufacturer has a limited budget for an item's advertisement, with this budget, it is only possible for him to market this item to limited users. How to select the most suitable users that will increase advertisement revenue? It seems to be an insurmountable problem to the existing recommendation algorithms. To address this issue, a new item orientated recommendation algorithm from the multi-view perspective is proposed in this paper. Different from the existing recommendation algorithms, this model provides the target items with the users that are the most possible to purchase them. The basic idea is to simultaneously calculate the relationships between items and the rating differences between users from a multi-view model in which the purchasing records of each user are regarded as a view and each record is seen as a node in a view. The experimental results show that our proposed method outperforms the state-of-the-art methods in the scenario of item orientated recommendation. (C) 2017 Elsevier B.V. All rights reserved.

关键词： recommendation algorithm Item orientated Multi-view learning

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Hybrid recommendation algorithm based on real-valued RBM and CNN

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第10期19卷 10673-10686页

作者： Wu, Jue Yang, Lei Yang, Fujun Zhang, Peihong Bai, Keqiang Southwest Univ Sci & Technol Sch Comp Sci & Technol Mianyang Sichuan Peoples R China China Aerodynam Res & Dev Ctr Computat Aerodynam Inst Mianyang Sichuan Peoples R China

With the unprecedented development of big data, it is becoming hard to get the valuable information hence, the recommendation system is becoming more and more popular. When the limited Boltzmann machine is used for collaborative filtering, only the scoring matrix is considered, and the influence of the item content, the user characteristics and the user evaluation content on the predicted score is not considered. To solve this problem, the modified hybrid recommendation algorithm based on Gaussian restricted Boltzmann machine is proposed in the paper. The user text information and the item text information are input to the embedding layer to change the text information into numerical vector. The convolutional neural network is used to get the latent feature vector of the text information. The latent vector is connected to rating vector to get the item and the user vector. The user vector and the item vector are fused together to get the user-item matrix which is input to the visual layer of Gaussian restricted Boltzmann Machine to predict the ratings. Some simulation experiments have been performed on the algorithm, and the results of the experiments proved that the algorithm is feasible.

关键词： convolutional neural network Gaussian restricted Boltzmann machine recommendation algorithm

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Network Representation Learning Enhanced recommendation algorithm

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IEEE ACCESS 2019年 7卷 61388-61399页

作者： Wang, Qiang Yu, Yonghong Gao, Haiyan Zhang, Li Cao, Yang Mao, Lin Dou, Kaiqi Ni, Wenye Nanjing Univ Posts & Telecommun Tongda Coll Yangzhou 225127 Jiangsu Peoples R China Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England

With the popularity of social network applications, more and more recommender systems utilize trust relationships to improve the performance of traditional recommendation algorithms. Social-network-based recommendation algorithms generally assume that users with trust relations usually share common interests. However, the performance of most of the existing social-network-based recommendation algorithms is limited by the coarse-grained and sparse trust relationships. In this paper, we propose a network representation learning enhanced recommendation algorithm. Specifically, we first adopt a network representation technique to embed social network into a low-dimensional space, and then utilize the low-dimensional representations of users to infer fine-grained and dense trust relationships between users. Finally, we integrate the fine-grained and dense trust relationships into the matrix factorization model to learn user and item latent feature vectors. The experimental results on real-world datasets show that our proposed approach outperforms traditional social-network-based recommendation algorithms.

关键词： Network representation learning recommendation algorithm matrix factorization social network

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An ecommerce recommendation algorithm based on link prediction

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ALEXANDRIA ENGINEERING JOURNAL 2022年第1期61卷 905-910页

作者： Liu, Guoguang Binzhou Univ Sch Econ & Management Binzhou 256600 Peoples R China

In the field of ecommerce, most recommendation algorithms are based on user-item bipartite graph network (BGN). But this kind of recommendation algorithm is severely lacking in accuracy and diversity. In this paper, a novel ecommerce recommendation algorithm is proposed based on BGN link prediction. Firstly, all the user-item data were imported into distance formula to calculate the similarity between the attributes. Then, the BGN was projected into a single-mode net-work (SMN), making it more efficient to extract potential links from the BGN. On this basis, the potential links were predicted based on similarity. Through experiments on real ecommerce data-sets, it was proved that our algorithm has a higher accuracy and coverage than typical recommen-dation algorithms. (c) 2021 THE AUTHOR. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University.

关键词： recommendation algorithm Bipartite graph network (BGN) Link prediction Ecommerce

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