Analysis of Personalized Recommendation Models Based on Multi-Dimensional Ontology

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.998-999.1347 ◽

2014 ◽

Vol 998-999 ◽

pp. 1347-1351

Author(s):

Xiao Yan Yuan

Keyword(s):

Domain Ontology ◽

Personalized Recommendation ◽

Single Dimension ◽

Model Based ◽

Knowledge Based ◽

Multiple Dimensions ◽

Information Recommendation

In order to solve the problem of information recommendation from single dimension, a personalized recommendation model based on multi-dimensional ontology is proposed in this paper. The necessity of multi-dimensional ontology is analyzed firstly. A multi-dimensional ontology, including domain ontology, time ontology and user ontology, etc., is then established using the examples of IT forefront knowledge. Based on this new ontology, recommendation of resources from multiple dimensions, i.e., the semantic, time, and comprehensive dimensions, is analyzed. Finally the personalized multi-dimensional recommendation model is presented.

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Research of Contextual Semantic Reasoning Model Based on Domain Ontology

Scientific Programming ◽

10.1155/2021/4011190 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Jinhai Li ◽

Yunlei Ma ◽

Xiang Zhan ◽

Jiaming Pei

Keyword(s):

Conditional Entropy ◽

Mobile Network ◽

Domain Ontology ◽

Mobile Environment ◽

Model Framework ◽

Semantic Reasoning ◽

Model Based ◽

Contextual Sensitivity ◽

Information Recommendation ◽

Mobile Context

With the development of mobile network technology and the popularization of mobile terminals, traditional information recommendation systems are gradually changing in the direction of real-time and mobile information recommendation. Information recommendation brings the problem of user contextual sensitivity within the mobile environment. For this problem, first, this paper constructs a domain ontology, which is applicable to the contextual semantic reasoning model. Second, based on the “5W + 1H” method, this paper constructs a context pedigree of the mobile environment using a model framework of a domain ontology. The contextual factors of the mobile environment are divided into six categories: the What-object context, the Where-place context, the When-time context, the Who-subject context, the Why-reason context, and the How-effect context. Then, considering the degree of influence of each contextual factor from the mobile context pedigree to the user is different, this paper uses contextual conditional entropy to calculate the contextual weight of each contextual attribute in the recommendation process. Based on this, a contextual semantic reasoning model based on a domain ontology is constructed. Finally, based on the open dataset provided by GroupLens, this paper verifies the validity and efficiency of the model through a simulation experiment.

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An interpretable mechanism for personalized recommendation based on cross feature

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202308 ◽

2021 ◽

pp. 1-12

Author(s):

Lv YE ◽

Yue Yang ◽

Jian-Xu Zeng

Keyword(s):

Recommender System ◽

Online Shopping ◽

Side Information ◽

Attention Mechanism ◽

Personalized Recommendation ◽

Model Based ◽

Embedded Model ◽

Feature Attention ◽

Match Score

The existing recommender system provides personalized recommendation service for users in online shopping, entertainment, and other activities. In order to improve the probability of users accepting the system’s recommendation service, compared with the traditional recommender system, the interpretable recommender system will give the recommendation reasons and results at the same time. In this paper, an interpretable recommendation model based on XGBoost tree is proposed to obtain comprehensible and effective cross features from side information. The results are input into the embedded model based on attention mechanism to capture the invisible interaction among user IDs, item IDs and cross features. The captured interactions are used to predict the match score between the user and the recommended item. Cross-feature attention score is used to generate different recommendation reasons for different user-items.Experimental results show that the proposed algorithm can guarantee the quality of recommendation. The transparency and readability of the recommendation process has been improved by providing reference reasons. This method can help users better understand the recommendation behavior of the system and has certain enlightenment to help the recommender system become more personalized and intelligent.

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Model-based diagnosis of the space shuttle main engine

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060400003036 ◽

1992 ◽

Vol 6 (3) ◽

pp. 131-148 ◽

Cited By ~ 6

Author(s):

Martin O. Hofmann ◽

Thomas L. Cost ◽

Michael Whitley

Keyword(s):

Space Shuttle ◽

Diagnostic System ◽

End User ◽

Reasoning Process ◽

Domain Experts ◽

Model Based ◽

Knowledge Based ◽

Modeling Analysis ◽

Engine Design ◽

Main Engine

The process of reviewing test data for anomalies after a firing of the Space Shuttle Main Engine (SSME) is a complex, time-consuming task. A project is under way to provide the team of SSME experts with a knowledge-based system to assist in the review and diagnosis task. A model-based approach was chosen because it can be adapted to changes in engine design, is easier to maintain, and can be explained more easily. A complex thermodynamic fluid system like the SSME introduces problems during modeling, analysis, and diagnosis which have as yet been insufficiently studied. We developed a qualitative constraint-based diagnostic system inspired by existing qualitative modeling and constraint-based reasoning methods which addresses these difficulties explicitly. Our approach combines various diagnostic paradigms seamlessly, such as the model-based and heuristic association-based paradigms, in order to better approximate the reasoning process of the domain experts. The end-user interface allows expert users to actively participate in the reasoning process, both by adding their own expertise and by guiding the diagnostic search performed by the system.

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Research of Personalized Recommendation Technology Based on Knowledge Graphs

Applied Sciences ◽

10.3390/app11157104 ◽

2021 ◽

Vol 11 (15) ◽

pp. 7104

Author(s):

Xu Yang ◽

Ziyi Huan ◽

Yisong Zhai ◽

Ting Lin

Keyword(s):

Neural Network ◽

Hot Spot ◽

Experimental Results ◽

Graph Representation ◽

Personalized Recommendation ◽

Knowledge Graph ◽

Learning Technology ◽

Data Set ◽

Model Based ◽

Knowledge Graphs

Nowadays, personalized recommendation based on knowledge graphs has become a hot spot for researchers due to its good recommendation effect. In this paper, we researched personalized recommendation based on knowledge graphs. First of all, we study the knowledge graphs’ construction method and complete the construction of the movie knowledge graphs. Furthermore, we use Neo4j graph database to store the movie data and vividly display it. Then, the classical translation model TransE algorithm in knowledge graph representation learning technology is studied in this paper, and we improved the algorithm through a cross-training method by using the information of the neighboring feature structures of the entities in the knowledge graph. Furthermore, the negative sampling process of TransE algorithm is improved. The experimental results show that the improved TransE model can more accurately vectorize entities and relations. Finally, this paper constructs a recommendation model by combining knowledge graphs with ranking learning and neural network. We propose the Bayesian personalized recommendation model based on knowledge graphs (KG-BPR) and the neural network recommendation model based on knowledge graphs(KG-NN). The semantic information of entities and relations in knowledge graphs is embedded into vector space by using improved TransE method, and we compare the results. The item entity vectors containing external knowledge information are integrated into the BPR model and neural network, respectively, which make up for the lack of knowledge information of the item itself. Finally, the experimental analysis is carried out on MovieLens-1M data set. The experimental results show that the two recommendation models proposed in this paper can effectively improve the accuracy, recall, F1 value and MAP value of recommendation.

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A Semantic Retrieval Model Based on Domain Ontology of Orchard Disease and Pests

Chinese Journal of Electronics ◽

10.1049/cje.2016.05.011 ◽

2016 ◽

Vol 25 (3) ◽

pp. 460-466 ◽

Cited By ~ 2

Author(s):

Jiajia Hou ◽

Hui Han ◽

Chengjing Qiu ◽

Dongmei Li

Keyword(s):

Domain Ontology ◽

Semantic Retrieval ◽

Retrieval Model ◽

Model Based

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A model-based expert system based on a domain ontology

Expert Systems ◽

10.1016/b978-012443880-4/50050-8 ◽

2002 ◽

pp. 171-196

Author(s):

Y KITAMURA ◽

M IKEDA ◽

R MIZOGUCHI

Keyword(s):

Expert System ◽

Domain Ontology ◽

Model Based

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Research on Semantic Retrieval System for the Document Knowledge Based on Domain Ontology

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.204-210.2171 ◽

2011 ◽

Vol 204-210 ◽

pp. 2171-2175

Author(s):

Zi Yu Liu ◽

Dong Li Zhang ◽

Xue Hui Li

Keyword(s):

High Speed ◽

Retrieval System ◽

Domain Ontology ◽

Experimental Results ◽

Semantic Retrieval ◽

High Speed Railway ◽

Knowledge Based ◽

Main Components ◽

Railway Domain ◽

Concept Similarity

Domain ontology can effectively organize the knowledge of that domain and make it easier to share and reuse. We can build domain ontology on thesaurus and thematic words and index document knowledge using domain ontology. Under which this paper designs a semantic retrieval system for the document knowledge based on domain ontology, and the system consists of four main components: ontology query, semantic precomputation for document and the concept similarity, semantic extended search and reasoning search. Finally, this paper makes an experiment on high-speed railway domain. The experimental results show that the developed semantic retrieval system can reach the satisfied recall and precision.

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