A Novel Travel Group Recommendation Model Based on User Trust and Social Influence

The interactions between group members often have a significant impact on the results of group recommendations. The traditional group recommendation algorithm does not consider the trust and social influence among users. It involves a low utilization rate of social relationship information, which leads to a low accuracy and satisfaction of group recommendations. Considering these issues, in this study, we propose a travel group recommendation model based on user trust and social influence. Based on the user trust relationship, this model defines the user direct and indirect trust and calculates the user global trust by combining the two trusts. Subsequently, the PageRank algorithm is used to calculate the social influence of users based on their interaction relationship history. Thereafter, a consensus model integrating the intra- and intergroup prediction scores is designed by integrating users’ global trust and social influence to realize group recommendations for tourist attractions. Comparison experiments with several well-known group recommendation models for datasets of different scenic spots in Beijing demonstrate that the proposed model provides a better recommendation performance.

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A Dynamic Information Dissemination Model Based on Implicit Link and Social Influence

IEEE Transactions on Computational Social Systems ◽

10.1109/tcss.2020.3044304 ◽

2020 ◽

pp. 1-10

Author(s):

Xinhong Wu ◽

Yunpeng Xiao ◽

Xia Liang ◽

Qian Li

Keyword(s):

Social Influence ◽

Information Dissemination ◽

Dynamic Information ◽

Model Based

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A Collaborative Filtering Algorithm with Intragroup Divergence for POI Group Recommendation

Applied Sciences ◽

10.3390/app11125416 ◽

2021 ◽

Vol 11 (12) ◽

pp. 5416

Author(s):

Yanheng Liu ◽

Minghao Yin ◽

Xu Zhou

Keyword(s):

Collaborative Filtering ◽

User Preference ◽

Computation Method ◽

Group Recommendation ◽

Preference Rating ◽

Filtering Method ◽

Personal Recommendation ◽

Collaborative Filtering Algorithm ◽

Group Members ◽

Individual Recommendation

The purpose of POI group recommendation is to generate a recommendation list of locations for a group of users. Most of the current studies first conduct personal recommendation and then use recommendation strategies to integrate individual recommendation results. Few studies consider the divergence of groups. To improve the precision of recommendations, we propose a POI group recommendation method based on collaborative filtering with intragroup divergence in this paper. Firstly, user preference vector is constructed based on the preference of the user on time and category. Furthermore, a computation method similar to TF-IDF is presented to compute the degree of preference of the user to the category. Secondly, we establish a group feature preference model, and the similarity of the group and other users’ feature preference is obtained based on the check-ins. Thirdly, the intragroup divergence of POIs is measured according to the POI preference of group members and their friends. Finally, the preference rating of the group for each location is calculated based on a collaborative filtering method and intragroup divergence computation, and the top-ranked score of locations are the recommendation results for the group. Experiments have been conducted on two LBSN datasets, and the experimental results on precision and recall show that the performance of the proposed method is superior to other methods.

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Intelligent design technology of automobile inspection tool based on 3D MBD model intelligent retrieval

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/09544070211000174 ◽

2021 ◽

pp. 095440702110001

Author(s):

Qi Cheng ◽

Shuchun Wang ◽

Xifeng Fang

Keyword(s):

Machine Learning ◽

Information Integration ◽

3D Model ◽

Product Information ◽

Design Method ◽

Equipment Design ◽

Design System ◽

Process Equipment ◽

Utilization Rate ◽

Model Based

The existing process equipment design resource utilization rate in automobile industry is low, so it is urgent to change the design method to improve the design efficiency. This paper proposed a fast design method of process equipment driven by classification retrieval of 3D model-based definition (MBD). Firstly, an information integration 3D model is established to fully express the product information definition and to effectively express the design characteristics of the existing 3D model. Through the classification machine-learning algorithm of 3D MBD model based on Extreme Learning Machine (ELM), the 3D MBD model with similar characteristics to the auto part model to be designed was retrieved from the complex process equipment case database. Secondly, the classification and retrieval of the model are realized, and the process equipment of retrieval association mapping with 3D MBD model is called out. The existing process equipment model is adjusted and modified to complete the rapid design of the process equipment of the product to be designed. Finally, a corresponding process equipment design system was developed and verified through a case study. The application of machine learning to the design of industrial equipment greatly shortens the development cycle of equipment. In the design system, the system learns from engineers, making them understand the design better than engineers. Therefore, it can help any user to quickly design 3D models of complex products.

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Deep Group Recommender System Model Based on User Trust

2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) ◽

10.1109/cisp-bmei51763.2020.9263556 ◽

2020 ◽

Author(s):

Yulong Song ◽

Wenming Ma ◽

Tongtong Liu

Keyword(s):

Recommender System ◽

System Model ◽

Model Based ◽

User Trust ◽

Group Recommender

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Stream-based explainable recommendations via blockchain profiling

Integrated Computer-Aided Engineering ◽

10.3233/ica-210668 ◽

2021 ◽

pp. 1-17

Author(s):

Fátima Leal ◽

Bruno Veloso ◽

Benedita Malheiro ◽

Juan Carlos Burguillo ◽

Adriana E. Chis ◽

...

Keyword(s):

Historical Data ◽

Positive Impact ◽

Empirical Evaluation ◽

Recommendation Systems ◽

Use Cases ◽

Collaborative Filter ◽

Model Based ◽

Collaborative Recommendation ◽

Smart Contract ◽

User Trust

Explainable recommendations enable users to understand why certain items are suggested and, ultimately, nurture system transparency, trustworthiness, and confidence. Large crowdsourcing recommendation systems ought to crucially promote authenticity and transparency of recommendations. To address such challenge, this paper proposes the use of stream-based explainable recommendations via blockchain profiling. Our contribution relies on chained historical data to improve the quality and transparency of online collaborative recommendation filters – Memory-based and Model-based – using, as use cases, data streamed from two large tourism crowdsourcing platforms, namely Expedia and TripAdvisor. Building historical trust-based models of raters, our method is implemented as an external module and integrated with the collaborative filter through a post-recommendation component. The inter-user trust profiling history, traceability and authenticity are ensured by blockchain, since these profiles are stored as a smart contract in a private Ethereum network. Our empirical evaluation with HotelExpedia and Tripadvisor has consistently shown the positive impact of blockchain-based profiling on the quality (measured as recall) and transparency (determined via explanations) of recommendations.

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