Efficient Context Management and Personalized User Recommendations in a Smart Social TV Environment

Aisopos, Fotis; Valsamis, Angelos; Psychas, Alexandros; Menychtas, Andreas; Varvarigou, Theodora

doi:10.1007/978-3-319-61920-0_8

Cited by 2 publications

(1 citation statement)

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“…Knowledge graph recommendation system is applied to many fields, such as smart social tv [17], personalized user recommendations [18][19][20], etc. However, in recent years, the movie knowledge graph has only just started.…”

Section: Related Workmentioning

confidence: 99%

Research of Personalized Recommendation Technology Based on Knowledge Graphs

et al. 2021

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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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Section: Related Workmentioning

confidence: 99%

Research of Personalized Recommendation Technology Based on Knowledge Graphs

et al. 2021

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Socialising around media

Tomás

Gutiérrez

Badii

et al. 2019

Multimed Tools Appl

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SAM is a social media platform that enhances the experience of watching video content in a conventional living room setting with a service that lets the viewer use a second screen (such as a smart phone) to interact with content, context and communities related to the main video content. This article describes three key functionalities used in the SAM platform in order to create an advanced interactive and social second screen experience for users: semantic analysis, context awareness and dynamic communities. Both dataset-based and end user evaluations of system functionalities are reported in order to determine the effectiveness and efficiency of the components directly involved and the platform as a whole.

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Efficient Context Management and Personalized User Recommendations in a Smart Social TV Environment

Cited by 2 publications

References 23 publications

Research of Personalized Recommendation Technology Based on Knowledge Graphs

Research of Personalized Recommendation Technology Based on Knowledge Graphs

Socialising around media

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