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检索条件"主题词=recommendation algorithm"

共 370 条记录，以下是81-90 订阅

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Research on An Extended SVD recommendation algorithm Based on User's Neighbor Model 7

Research on An Extended SVD Recommendation Algorithm Based o...

引用

7th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Pan, Mengyuan Yang, Yang Mi, Zhenqiang Univ Sci & Technol Beijing Dept Comp & Commun Engn Beijing Peoples R China

ISBN: (纸本)9781467399043

The fact that Singular Value Decomposition(SVD) algorithm can be used to reduce dimension and remove noise with good scalability and accuracy makes it been widely adopted in recommendation systems. However, the valuable association information between user-user, item-item are neglected in traditional SVD. Therefore, we proposed a new extended SVD algorithm, named USVD in this paper to improve the algorithm's accuracy. USVD corrects SVD's prediction error of the target user according to the known prediction errors of his neighbors. Because SVD may lose user similarity information during the process of Matrix decomposition, we modified the least squares problem of SVD. Meanwhile, we also made improvement on the calculation of traditional Pearson Correlation Coefficient(pCCs). The proposed USVD is evaluated by extensive experiments with MovieLens datasets under RMSE index. Results show that USVD achieves better accuracy and higher efficiency compared to contemporary schemes.

关键词： recommendation algorithm Roting prediction Similarity SVD RMSE

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LBS and Multidimensional Scoring Based recommendation algorithm 19

LBS and Multidimensional Scoring Based Recommendation Algori...

引用

3rd International Conference on Computer Science and Application Engineering (CSAE)

作者： Chen, Dongming Meng, Qinghua Wu, Xiaoguang Huang, Xinyu Wang, Dongqi Northeastern Univ Software Coll Shenyang Peoples R China

ISBN: (纸本)9781450362948

With the rapid development of the national economy, China's car ownership is increasing, so the demand and value of the automobile after-service market are constantly rising. How to integrate the existing automobile maintenance and repair services to provide fast and convenient environment for car owners has become a difficult problem. Firstly, in view of the sparsity of user-store scoring matrix, this paper proposes geolocation-based matrix partition to reduce the running time. Then demographic-based recommendation algorithm is employed to solve the 'cold start' problem existed in user-based collaborative filtering recommendation algorithm. Finally, in terms of different users' concerns, a dynamic multidimensional scoring model is proposed. The experiment is conducted on the Meituan data, and the results show that the proposed method achieves good performance in recommendation efficiency, precision and recall. Meanwhile, this method also brings light to the future automobile after-service market.

关键词： LBS Matrix partition Multidimensional scoring recommendation algorithm

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An Improved Collaborative Filtering recommendation algorithm 4

An Improved Collaborative Filtering Recommendation Algorithm

引用

4th IEEE International Conference on Big Data Analytics (ICBDA)

作者： Wang Hong-xia Beijing Youth Polit Coll Sch Informat Media & Art Beijing Peoples R China

ISBN: (纸本)9781728112824

Collaborative Filtering recommendation (CFR) is the earliest proposed and widest used method in recommendation system. It can not only find out what people are interested in at present but also mine out the implicit interest information. As time goes by and data volumes grow, recommendations effect can be improved significantly. Therefore, the collaborative filtering recommendation is one of the most popular recommendation technologies in the electronic commerce recommendation system. But the algorithm also faces some problems such as cold start, sparse, real-time and scalability etc. problems affecting the quality of recommendation. An improved collaborative filtering algorithm based on properties is presented in this paper to improve the utilization of data resources and decrease the sparse degree of matrix. Meanwhile, in order to reduce the similarity calculation quantity of work and filter dirty data effectively, the collaborative filtering algorithm based on clustering and singular value decomposition is put forwards to improve the effect of collaborative filtering algorithm. At last the effectiveness of the improved collaborative algorithm in this paper is verified through experiments.

关键词： collaborative filtering recommendation algorithm clustering singular value decomposition

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Top-N Collaborative Filtering recommendation algorithm Based on Knowledge Graph Embedding 14th

Top-N Collaborative Filtering Recommendation Algorithm Based...

引用

14th International Conference on Knowledge Management in Organizations (KMO) - Synergistic Role of Knowledge Management in Organization

作者： Zhu, Ming Zhen, De-sheng Tao, Ran Shi, You-qun Feng, Xiang-yang Wang, Qian Donghua Univ Sch Comp Sci & Technol Shanghai 201620 Peoples R China

ISBN: (纸本)9783030214517;9783030214500

The traditional collaborative filtering recommendation algorithm only uses the item-user rating matrix without considering the semantic information of the item itself, resulting in a problem that the recommendation accuracy is not high. This paper proposes a Top-N collaborative filtering recommendation algorithm based on knowledge graph embedding. The knowledge graph embedding is used to learn a low-dimensional vector for each entity and relationship in the knowledge graph, while maintaining the structure and semantic information of the original graph in the vector. By calculating the semantic similarity between items, the semantic information of the item itself is incorporated into the collaborative filtering recommendation. The algorithm makes up for the defect that the collaborative filtering recommendation algorithm does not consider the knowledge information of the item itself, and enhances the effect of collaborative filtering recommendation on the semantic level. The experimental results on the MovieLens dataset show that the algorithm can get higher values on precision, recall and F1 measure.

关键词： Collaborative filtering Knowledge graph embedding recommendation algorithm Semantic similarity

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Matrix Factorization recommendation algorithm Incorporating Tag Factor 4

Matrix Factorization Recommendation Algorithm Incorporating ...

引用

IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Lu, Mengmeng Tian, Pei Commun Univ China Sch Informat Engn Beijing Peoples R China

ISBN: (纸本)9781538653739

Matrix factorization is a hot spot in the research of recommendation algorithms. Traditional matrix factorization algorithm only learns the user factor and the item factor from rating data, not fully considering the influence of the tag data. Therefore, a matrix factorization recommendation algorithm incorporating tag factor is proposed. This algorithm obtains user's tag preference matrix by considering rating data and tag data comprehensively, and then incorporates item preference tag factor and user's tag preference factor into the matrix factorization recommendation algorithm. In this algorithm, tag-rating sparse coefficient is proposed to better balance the use of latent factors and tag factors in the recommendation process. At the same time, the TF-IDF algorithm is used to calculate the tag factor weight of the item, which reflects the influence of different times of the tag marked on different items. Experimental results demonstrate that the proposed recommendation algorithm can produce better accuracy and performance compared with the traditional matrix factorization algorithm.

关键词： recommendation algorithm matrix factorization tag TF-IDF

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Low-Rank and Sparse Cross-Domain recommendation algorithm 23rd

引用

23rd International Conference on Database Systems for Advanced Applications (DASFAA).

作者： Zhao, Zhi-Lin Huang, Ling Wang, Chang-Dong Huang, Dong Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China South China Agr Univ Coll Math & Informat Guangzhou Peoples R China

ISBN: (纸本)9783319914527;9783319914510

In this paper, we propose a novel Cross-Domain Collaborative Filtering (CDCF) algorithm termed Low-rank and Sparse Cross-Domain (LSCD) recommendation algorithm. Different from most of the CDCF algorithms which tri-factorize the rating matrix of each domain into three low dimensional matrices, LSCD extracts a user and an item latent feature matrix for each domain respectively. Besides, in order to improve the performance of recommendations among correlated domains by transferring knowledge and uncorrelated domains by differentiating features in different domains, the features of users are separated into shared and domain-specific parts adaptively. Specifically, a low-rank matrix is used to capture the shared feature subspace of users and a sparse matrix is used to characterize the discriminative features in each specific domain. Extensive experiments on two real-world datasets have been conducted to confirm that the proposed algorithm transfers knowledge in a better way to improve the quality of recommendation and outperforms the state-of-the-art recommendation algorithms.

关键词： Low-rank Sparse Cross-domain recommendation algorithm

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Research on collaborative filtering recommendation algorithm based on user behavior characteristics

Research on collaborative filtering recommendation algorithm...

引用

International Conference on Big Data and Artificial Intelligence and Software Engineering (ICBASE)

作者： Mao Jianjun Yunxiang Technol Co Ltd Nanjing Peoples R China

ISBN: (纸本)9781728196190

The recommendation system of the website can not only recommend products for users and save users the time spent searching for products, but also help practitioners reduce unnecessary sales activities and management expenses. The successful use of the recommendation system can simplify the user's purchase process, improve the user's shopping satisfaction, and can help companies carry out more precise marketing strategies, increase corporate marketing revenue, and increase sales profits. In the context of increasing data volume, the decline in accuracy in the actual recommendation process has caused more and more sales problems. By analyzing and extracting the behavioral characteristics of users visiting the website, this paper proposes a collaborative filtering algorithm based on user behavioral characteristics. Based on the behavioral characteristics shown by the user's page dwell time and number of visits when shopping on the website, the collaborative filtering algorithm is used to find out The shared behavioral characteristics of users during the visit process, recommend potential products of interest to new users, and improve the accuracy of the service and sales of the website. The algorithm can improve the recommendation accuracy and coverage of the recommendation system.

关键词： behavior characteristics collaborative filtering recommendation algorithm

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An Attention-based recommendation algorithm 17

An Attention-based Recommendation Algorithm

引用

IEEE Int Conf on Parallel and Distributed Processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking (ISPA/BDCloud/SocialCom/SustainCom)

作者： Chu, Yan Qi, Shuhao Yang, Yue Shan, Chenqi Wang, Lina Wang, Zhengkui Harbin Engn Univ Coll Comp Sci & Technol Harbin Peoples R China Singapore Inst Technol InfoComm Technol Singapore Singapore

ISBN: (纸本)9781728143286

Conventional recommendation algorithms adopt collaborative filtering approach to recommend items to one user through rating history from other users who share similar preferences. However, this method lacks of consideration of the interest levels, as the rating behavior of users varies with different evaluation standards. In this paper, we introduce one Attention-based recommendation algorithm (ABRA), which makes full use of users' comment information to make more reasonable recommendations. ABRA facilitates emotion recognition by analyzing the emotions from users' comments and identifying the polarity of emotions by designing an emotional attention-based model. ABRA integrates the emotional attention model into the Recurrent Neural Network (RNN) to train the comments in order to extract the emotion vector, which is further used to classify the comments. Meanwhile, the context attention mechanism is used in the RNN to identify the emotional polarity of each word. This enables model to identify not only the emotion polarity, but also the tendency value of the emotion words for constructing one more accurate and efficient emotion vector. In addition, we adopt the score correcting gate to decide whether the users' rating operation needs to be updated or not. A modified user-item matrix is applied to recommendation and collaborative filtering recommendation is carried out for users according to emotional score. We train the ABRA using movie review data from Cornell University, and test the model using the Amazon Movies&TV and Yelp datasets. The experimental results indicate that, compared with conventional recommendation algorithms, ABRA can more reasonable perceive the users' emotions and make more accurate recommendation.

关键词： recommendation algorithm attention-based recurrent neural network

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Graph Convolution recommendation algorithm Integrating Multi-relationship Preferences 20th

Graph Convolution Recommendation Algorithm Integrating Multi...

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20th International Conference on Intelligent Computing (ICIC)

作者： Su, Jing Zhao, Tianfeng Wu, Jianghong Shi, Peixuan Tianjin Univ Sci & Technol Sch Artificial Intelligence Tianjin 300457 Peoples R China

ISBN: (纸本)9789819755875;9789819755882

With the continuous development of Internet technology, existing data forms are becoming more and more complex. However, traditional recommendation algorithms have limitations when processing complex structured data. This paper improves the message propagation layer by considering user social relationships and item similarity relationships. Firstly, An item similarity calculation method that combines user activity is proposed, which combines social relationships and improved item similarity information with interaction information to represent the embedding of users and items. Secondly, the attention mechanism is integrated in the process of aggregating information to make the model more interpretable. Finally, high-order transfer of information is achieved by stacking graph convolutional layers. And perform inner product operation on the user and item representations obtained in the propagation layer to achieve prediction. Experimental verification was conducted on the Lastfm data set. The experimental results show that the performance of this method is the best, proving the effectiveness of the method.

关键词： recommendation algorithm Graph convolutional neural network Social relationship Item similarity Attention mechanism

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AN IMPROVED recommendation algorithm VIA SOCIAL BEHAVIORS 12

AN IMPROVED RECOMMENDATION ALGORITHM VIA SOCIAL BEHAVIORS

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12th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Liu, Jin-Hu Zhu, Yu-Xiao Shi, Kun-Yu Univ Elect Sci & Technol China Web Sci Ctr Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu 611731 Peoples R China

ISBN: (纸本)9781467382663

recommendation is an important problem in the traditional field of data mining. As a consequence, various kinds of algorithms have been proposed in the last few years to improve the recommendation performance. However, many of them overlook users' rating behaviors. In this paper, an improved recommendation algorithm with considering users' habits and rating behaviors will be proposed. Firstly, calculate the ratings entropy for each user via users' rating records which can reflect users' behavioral features. Secondly, widely-used user-based method will be improved by considering rating entropy. Finally, the predicting rate is generated. Experimental results on the three datasets: Movie Lens, Netflix and RYM all suggest that the proposed method can enhance the algorithmic accuracy. Furthermore, since this method can improve those missing value more accurately via users' similarity and rating behaviors which might shed some light on discovering users' purchasing intention and optimizing the performance of recommender systems by human dynamics.

关键词： recommendation algorithm similarity human dynamics data mining

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