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A movie recommendation algorithm based on genre correlations

EXPERT SYSTEMS WITH APPLICATIONS 2012年第9期39卷 8079-8085页

作者： Choi, Sang-Min Ko, Sang-Ki Han, Yo-Sub Yonsei Univ Dept Comp Sci Seoul 120749 South Korea

Since the late 20th century, the number of Internet users has increased dramatically, as has the number of Web searches performed on a daily basis and the amount of information available. A huge amount of new information is transferred to the Web on a daily basis. However, not all data are reliable and valuable, which implies that it may become more and more difficult to obtain satisfactory results from Web searches. We often iterate searches several times to find what we are looking for. To solve this problem, researchers have suggested the use of recommendation systems. Instead of searching for the same information several times, a recommendation system proposes relevant information. In the Web 2.0 era, recommendation systems often rely on collaborative filtering by users. In general, a collaborative filtering approach based on user information such as gender, location, or preference is effective. However, the traditional approach can fail due to the cold-start problem or the sparsity problem, because initial user information is required for this approach to be effective. Recently, several attempts have been made to tackle these collaborative filtering problems. One such attempt used category correlations of contents. For instance, a movie has genre information provided by movie experts and directors. This category information is more reliable than user ratings. Moreover, newly created content always has category information, allowing avoidance of the cold-start problem. In this study, we consider a movie recommendation system and improve the previous algorithms based on genre correlations to correct its shortcomings. We also test the modified algorithm and analyze the results with respect to two characteristics of genre correlations. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： recommendation algorithm Genre correlation Cold-start problem Sparsity problem

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A Novel recommendation algorithm Integrates Resource Allocation and Resource Transfer in Weighted Bipartite Network

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Big Data Mining and Analytics 2024年第2期7卷 357-370页

作者： Qiang Sun Leilei Shi Lu Liu Zixuan Han Liang Jiang Yan Wu Yeling Zhao School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang 212013China School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK School of Ocean College Jiangsu University of Science and TechnologyZhenjiang 212003China School of Finance and Economics Jiangsu UniversityZhenjiang 212013China

Grid-based recommendation algorithms view users and items as abstract nodes,and the information utilised by the algorithm is hidden in the selection relationships between users and *** these relationships can be easily handled,much useful information is overlooked,resulting in a less accurate recommendation *** aim of this paper is to propose improvements on the standard substance diffusion algorithm,taking into account the influence of the user’s rating on the recommended item,adding a moderating factor,and optimising the initial resource allocation vector and resource transfer matrix in the recommendation *** average ranking score evaluation index is introduced to quantify user satisfaction with the recommendation *** are conducted on the MovieLens training dataset,and the experimental results show that the proposed algorithm outperforms classical collaborative filtering systems and network structure based recommendation systems in terms of recommendation accuracy and hit rate.

关键词： cloud computing link prediction bipartite graph network recommendation algorithm cold start problem

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A Novel Social Situation Analytics-Based recommendation algorithm for Multimedia Social Networks

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IEEE ACCESS 2019年 7卷 117749-117760页

作者： Zhang, Zhiyong Sun, Ranran Choo, Kim-Kwang Raymond Fan, Kefeng Wu, Wei Zhang, Manhua Zhao, Changwei Henan Univ Sci & Technol Informat Engn Coll Luoyang 471023 Peoples R China Univ Texas San Antonio Dept Informat Syst & Cyber Secur San Antonio TX 78249 USA China Elect Standardizat Inst Beijing 100007 Peoples R China Skyworth Grp Co Ltd Shenzhen 518057 Peoples R China Shenzhen Chuangwei RGB Elect Co Ltd Shenzhen 518108 Peoples R China

Due to the increasing popularity of multimedia social networks (MSNs), the ability to mine users' interests in different contexts on such networks is crucial in recommendation systems. It is, however, challenging to mine users' current preferences based on session on MSNs. In this study, we propose a novel recommendation algorithm based on both social situation analytics and collaborative filtering for session-based recommendation. Specifically, the algorithm predicts the rating for target users based on their nearest neighbors and historical behaviors. First, for the purpose of mining users' current intentions, the session-level behavioral sequences of target user are analyzed based on SocialSitu (t). Then, the recommended contents are generated for target users based on their behaviors and perceived session-based intentions and identity. We evaluate the performance of the proposed algorithm using real-world social media dataset from Shareteches. Findings demonstrate that our algorithm outperforms two classical algorithms and a state-of-the-art method.

关键词： Multimedia social networks situation analytics behavior analysis recommendation algorithm prototype

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A crowd-sourcing recommendation algorithm OPCA-CF using outer-product co-attention mechanism

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NONDESTRUCTIVE TESTING AND EVALUATION 2024年第1期39卷 102-124页

作者： Bi, Kejun Liu, Jingwen Zhao, Qiwen Chen, Yanru Xing, Bin Guo, Bing Sichuan Univ Sch Comp Sci Chengdu Peoples R China Chongqing Innovat Ctr Ind Big Data Co Ltd Chongqing Peoples R China

With the rapid development of information technology, crowd-sourcing technology is increasingly used in non-invasive monitoring in smart cities. Applying recommendation algorithms in crowd-sourcing can optimise resource allocation, improve task-matching accuracy and enhance participant satisfaction, whereas existing recommendation algorithms cannot be directly applied in crowd-sourcing, as such scenarios have unique features, such as task timeliness and multi-role users. Designed explicitly for crowd-sourcing scenarios, our OPCA-CF (Outer-product Co-attention Collaborative Filtering) algorithm is formed by an upgraded ItemCF (Item-based Collaborative Filtering) algorithm as main-network and OPCA (Outer-product Co-attention) mechanism as a sub-network. Firstly, ItemCF is improved through attribute-level task feature learning, new-role feature and weighted cross-entropy in the loss function. Most importantly, we propose OPCA using outer-product, while the existing co-attention mechanism only uses inner-product. Compared with the best existing algorithm using real-world datasets, OPCA-CF's performance is proved to be superior by 1.24%, 4.25% and 5.35%, with binary classification indicators AUC (Area under Curve), recommended Lists related indicators HR (Hit Ratio) and MRR (Mean Reciprocal Rank), respectively. All the performance indicators verified the effectiveness of the OPCA-CF algorithm.

关键词： recommendation algorithm attention mechanism crowd-sourcing collaborative filtering

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Multimodal recommendation algorithm based on Dempster-Shafer evidence theory

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第10期83卷 28689-28704页

作者： Wang, Xiaole Qin, Jiwei Xinjiang Univ Sch Informat Sci & Engn Urumqi 830046 Peoples R China Xinjiang Univ Key Lab Signal Detect & Proc Urumqi 830046 Peoples R China

Multimodal features of items (e.g., text, audio, visual) have been proven to be effective in improving recommendations. Most of the existing personalized recommendation algorithms only use user-item interaction to recommend items for users, ignoring the rich auxiliary information implicit in the multimodal features of items. This auxiliary information can enrich the user's description of the item and enhance the mining ability of the recommendation algorithm. However, as a result of users having different preferences for each modality, the multimodal recommendation becomes a challenging task. Therefore, this paper proposes a multimodal recommendation algorithm based on the Dempster-Shafer evidence theory. First, we use the similarity measure of user history interaction items and non-interaction items in different modalities to characterize user preferences for noninteraction items. Second, we use the discrete degree of different modal features of user history interaction items to represent the degree of user preference for different modalities. Third, according to the Dempster-Shafer evidence theory, users' preferences for noninteractive items in different modalities are considered evidence. As a result, we fuse this evidence with different modal preferences to produce recommendations. Experimental results show that the proposed method is superior to the traditional personalized recommendation algorithm and the early multimodal fusion recommendation method. The proposed algorithm has good interpretability and expansibility.

关键词： recommendation algorithm Multimodal Evidence theory Fusion

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An intelligent deep learning-enabled recommendation algorithm for teaching music students

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SOFT COMPUTING 2022年第20期26卷 10591-10598页

作者： Tang, Changfei Zhang, Jun Hunan Normal Univ Mus Coll Changsha Peoples R China South Univ Sch Architecture & Art Cent Changsha Peoples R China

The existing research on data analysis and personalized recommendation algorithms for music mainly focuses on improving traditional recommendation systems such as classical collaborative filtering recommendation, content recommendation, and model recommendation. However, there has been no efficient solution to the cold start of recommendation systems and the sparse nature of scoring matrices. There is a dire need for an intelligent and efficient algorithm that can support the students in teaching music in an effective way. Deep belief neural network (DBN-DNN) is a deep learning model nested by many restricted Boltzmann machines (RBMs) to deal with sparse matrices and immediacy. At the same time, it can generate better initialization parameters and significantly improve the training speed of the model. This paper utilizes a decision support system that decides the matrix decomposition of user ratings by using a hidden semantic model to extract user preference features for hidden factors and the weights of music pieces on these k hidden factors. The proposed approach extracts the audio feature values from music clips by considering that music itself has classifiable information. This method is intelligent in terms of efficiency for effective music teaching and helps students learn. The experiments show that the personalized recommendation algorithm combined with a deep belief neural network has good recommendation performance. The recommendation coefficient predicted by the model is lower than the real rating for the samples with ratings. The recommendation is no longer limited to the original user group and song library and has better recommendation accuracy and scalability.

关键词： Music Deep learning recommendation algorithm Instructional design

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Short Video recommendation algorithm Incorporating Temporal Contextual Information and User Context

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Computer Modeling in Engineering & Sciences 2023年第4期135卷 239-258页

作者： Weihua Liu Haoyang Wan Boyuan Yan Network Security and Information Management Center Jining UniversityJining272000China School of Computer Science Qufu Normal UniversityRizhao276800China

With the popularity of 5G and the rapid development of mobile terminals,an endless stream of short video software *** short-form mobile video in fragmented time has become the mainstream of user’s ***,designing an efficient short video recommendation method has become important for major network platforms to attract users and satisfy their ***,the explosive growth of data leads to the low efficiency of the algorithm,which fails to distill users’points of interest on one hand *** the other hand,integrating user preferences and the content of items urgently intensify the requirements for platform *** this paper,we propose a collaborative filtering algorithm,integrating time context information and user context,which pours attention into expanding and discovering user *** the first place,we introduce the temporal context information into the typical collaborative filtering algorithm,and leverage the popularity penalty function to weight the similarity between recommended short videos and the historical short *** remains one more *** also introduce the user situation into the traditional collaborative filtering recommendation algorithm,considering the context information of users in the generation recommendation stage,and weight the recommended short-formvideos of *** last,a diverse approach is used to generate a Top-K recommendation list for *** through a case study,we illustrate the accuracy and diversity of the proposed method.

关键词： recommendation algorithm user contexts short video temporal contextual information

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

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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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