Information Fusion-Based Deep Neural Attentive Matrix Factorization Recommendation

The emergence of the recommendation system has effectively alleviated the information overload problem. However, traditional recommendation systems either ignore the rich attribute information of users and items, such as the user’s social-demographic features, the item’s content features, etc., facing the sparsity problem, or adopt the fully connected network to concatenate the attribute information, ignoring the interaction between the attribute information. In this paper, we propose the information fusion-based deep neural attentive matrix factorization (IFDNAMF) recommendation model, which introduces the attribute information and adopts the element-wise product between the different information domains to learn the cross-features when conducting information fusion. In addition, the attention mechanism is utilized to distinguish the importance of different cross-features on prediction results. In addition, the IFDNAMF adopts the deep neural network to learn the high-order interaction between users and items. Meanwhile, we conduct extensive experiments on two datasets: MovieLens and Book-crossing, and demonstrate the feasibility and effectiveness of the model.

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Recommendation Based on Trust Diffusion Model

The Scientific World JOURNAL ◽

10.1155/2014/159594 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Jinfeng Yuan ◽

Li Li

Keyword(s):

Matrix Factorization ◽

High Performance ◽

Recommendation System ◽

Online Information ◽

Trust Network ◽

Data Sparsity ◽

Pmf Model ◽

Probabilistic Matrix Factorization ◽

Sparsity Problem ◽

Relationship Of

Recommender system is emerging as a powerful and popular tool for online information relevant to a given user. The traditional recommendation system suffers from the cold start problem and the data sparsity problem. Many methods have been proposed to solve these problems, but few can achieve satisfactory efficiency. In this paper, we present a method which combines the trust diffusion (DiffTrust) algorithm and the probabilistic matrix factorization (PMF). DiffTrust is first used to study the possible diffusions of trust between various users. It is able to make use of the implicit relationship of the trust network, thus alleviating the data sparsity problem. The probabilistic matrix factorization (PMF) is then employed to combine the users' tastes with their trusted friends' interests. We evaluate the algorithm on Flixster, Moviedata, and Epinions datasets, respectively. The experimental results show that the recommendation based on our proposed DiffTrust + PMF model achieves high performance in terms of the root mean square error (RMSE), Recall, andFMeasure.

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Missing Data Recovery Based on Probability Matrix Factorization Algorithm with prior information fusion

Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering ◽

10.1145/3443467.3443746 ◽

2020 ◽

Author(s):

Ge Liu ◽

Guosheng Rui ◽

Wenbiao Tian ◽

Liyao Wu ◽

Junyi Huang

Keyword(s):

Missing Data ◽

Information Fusion ◽

Matrix Factorization ◽

Prior Information ◽

Data Recovery ◽

Factorization Algorithm

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Personalized Recommendation Model of High-Quality Education Resources for College Students Based on Data Mining

Complexity ◽

10.1155/2021/9935973 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Chaohua Fang ◽

Qiuyun Lu

Keyword(s):

Learning Process ◽

Data Science ◽

Recommendation System ◽

Information Overload ◽

Rapid Development ◽

Learning Ability ◽

Personalized Recommendation ◽

Learning Resources ◽

Learner Model ◽

Internet Education

With the rapid development of information technology and data science, as well as the innovative concept of “Internet+” education, personalized e-learning has received widespread attention in school education and family education. The development of education informatization has led to a rapid increase in the number of online learning users and an explosion in the number of learning resources, which makes learners face the dilemma of “information overload” and “learning lost” in the learning process. In the personalized learning resource recommendation system, the most critical thing is the construction of the learner model. Currently, most learner models generally have a lack of scientific focus that they have a single method of obtaining dimensions, feature attributes, and low computational complexity. These problems may lead to disagreement between the learner’s learning ability and the difficulty of the recommended learning resources and may lead to the cognitive overload or disorientation of learners in the learning process. The purpose of this paper is to construct a learner model to support the above problems and to strongly support individual learning resources recommendation by learning the resource model which effectively reduces the problem of cold start and sparsity in the recommended process. In this paper, we analyze the behavioral data of learners in the learning process and extract three features of learner’s cognitive ability, knowledge level, and preference for learning of learner model analysis. Among them, the preference model of the learner is constructed using the ontology, and the semantic relation between the knowledge is better understood, and the interest of the student learning is discovered.

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AANMF: Attribute-Aware Attentional Neural Matrix Factorization

Information Technology And Control ◽

10.5755/j01.itc.48.4.23149 ◽

2019 ◽

Vol 48 (4) ◽

pp. 682-693

Author(s):

Bo Zheng ◽

Jinsong Hu

Keyword(s):

Matrix Factorization ◽

Recommendation System ◽

Auxiliary Information ◽

Inner Product ◽

Data Sets ◽

It Projects ◽

Real World Data ◽

Latent Space ◽

Almost All ◽

Novel Model

Matrix Factorization (MF) is one of the most intuitive and effective methods in the Recommendation System domain. It projects sparse (user, item) interactions into dense feature products which endues strong generality to the MF model. To leverage this interaction, recent works use auxiliary information of users and items. Despite effectiveness, irrationality still exists among these methods, since almost all of them simply add the feature of auxiliary information in dense latent space to the feature of the user or item. In this work, we propose a novel model named AANMF, short for Attribute-aware Attentional Neural Matrix Factorization. AANMF combines two main parts, namely, neural-network-based factorization architecture for modeling inner product and attention-mechanism-based attribute processing cell for attribute handling. Extensive experiments on two real-world data sets demonstrate the robust and stronger performance of our model. Notably, we show that our model can deal with the attributes of user or item more reasonably. Our implementation of AANMF is publicly available at https://github.com/Holy-Shine/AANMF.

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A New Weighted-learning Approach for Exploiting Data Sparsity in Tag-based Item Recommendation Systems

International Journal of Intelligent Engineering and Systems ◽

10.22266/ijies2021.0228.36 ◽

2021 ◽

Vol 14 (1) ◽

pp. 387-399

Author(s):

Noor Ifada ◽

◽

Richi Nayak ◽

Keyword(s):

Recommendation System ◽

Recommendation Systems ◽

Learning Approach ◽

Tensor Model ◽

Learning Approaches ◽

Data Sparsity ◽

Tensor Models ◽

Sparsity Problem ◽

Weighted Rank

The tag-based recommendation systems that are built based on tensor models commonly suffer from the data sparsity problem. In recent years, various weighted-learning approaches have been proposed to tackle such a problem. The approaches can be categorized by how a weighting scheme is used for exploiting the data sparsity – like employing it to construct a weighted tensor used for weighing the tensor model during the learning process. In this paper, we propose a new weighted-learning approach for exploiting data sparsity in tag-based item recommendation system. We introduce a technique to represent the users’ tag preferences for leveraging the weighted-learning approach. The key idea of the proposed technique comes from the fact that users use different choices of tags to annotate the same item while the same tag may be used to annotate various items in tag-based systems. This points out that users’ tag usage likeliness is different and therefore their tag preferences are also different. We then present three novel weighting schemes that are varied in manners by how the ordinal weighting values are used for labelling the users’ tag preferences. As a result, three weighted tensors are generated based on each scheme. To implement the proposed schemes for generating item recommendations, we develop a novel weighted-learning method called as WRank (Weighted Rank). Our experiments show that considering the users' tag preferences in the tensor-based weightinglearning approach can solve the data sparsity problem as well as improve the quality of recommendation.

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Dimensionality Reduction with Category Information Fusion and Non-negative Matrix Factorization for Text Categorization

Artificial Intelligence and Computational Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-642-23896-3_62 ◽

2011 ◽

pp. 505-512 ◽

Cited By ~ 3

Author(s):

Wenbin Zheng ◽

Yuntao Qian ◽

Hong Tang

Keyword(s):

Dimensionality Reduction ◽

Information Fusion ◽

Matrix Factorization ◽

Text Categorization ◽

Category Information ◽

Non Negative Matrix Factorization

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A Mobile Service Recommendation System Using Multi-Criteria Ratings

International Journal of Interdisciplinary Telecommunications and Networking ◽

10.4018/jitn.2010100103 ◽

2010 ◽

Vol 2 (4) ◽

pp. 30-40 ◽

Cited By ~ 1

Author(s):

Zhuang Shao ◽

Zhikui Chen ◽

Xiaodi Huang

Keyword(s):

Recommendation System ◽

Information Overload ◽

Mobile Services ◽

Rank Aggregation ◽

Mobile Service ◽

Service Recommendation ◽

Service Creation ◽

Multi Agent ◽

Individual Criterion ◽

Service Ranking

With the rapid advancement of wireless technologies and mobile devices, mobile services offer great convenience and huge opportunities for service creation. However, information overload make service recommendation become a crucial issue in mobile services. Although traditional single-criteria recommendation systems have been successful in a number of personalization applications, obviously individual criterion cannot satisfy consumers’ demands. Relying on multi-criteria ratings, this paper presents a novel recommendation system using the multi-agent technology. In this system, the ratings with respect to the three criteria are aggregated into an overall service ranking list by a rank aggregation algorithm. Furthermore, all of the services are classified into several clusters to reduce information overload further. Finally, Based on multi-criteria rank aggregation, the prototype of a recommendation system is implemented. Successful applications of this recommendation system have demonstrated the efficiency of the proposed approach.

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Research on User Clustering of Recommended System Based on Fuzzy Clustering

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.513-517.1540 ◽

2014 ◽

Vol 513-517 ◽

pp. 1540-1544

Author(s):

Li Hua Zhang ◽

Wei Liu

Keyword(s):

Fuzzy Clustering ◽

Clustering Analysis ◽

Recommendation System ◽

Information Overload ◽

Fuzzy Clustering Analysis ◽

User Clustering ◽

Objective Classification ◽

Distance Education System ◽

High Quality Service

Today's society is a society of information explosion, the popularity of the Internet and development bring a lot of convenience to people, people can easily get a lot of information on the network, however, facing so many information, people prone to the problems of "information overload" and "resources disorientation. Therefore, the recommended system came into being, the recommendation system can provide people with the most in need and most concern to avoid the time of the search and comparison. This article intends to use the very mature recommendation system in the field of electronic commerce to distance education system and promotes personalized learning, shifting the traditional "what teachers teach, what students receive" to "what the students need, what the system provides, which is consistent of constructivism study philosophy. The analysis of users interested as the basis of the recommendation system, users clustering is very important, the objective classification of fuzzy clustering analysis can recommend for users to enjoy high-quality service.

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