Top-N Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph Embedding

Zhu, Ming; Zhen, De-sheng; Tao, Ran; Shi, Youqun; 馮, 向陽; Wang, Qian

doi:10.1007/978-3-030-21451-7_11

Cited by 13 publications

(3 citation statements)

References 7 publications

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“…This paper compares the algorithm IFT‐PTransE with PTransE‐ CF [30], CCVR [12], Seq4Rec [32], KGECF [33], and verifies the effectiveness of the IFT‐PTransE algorithm in the personalized video recommendation problem, The experimental results are analyzed from three aspects of recommendation accuracy, recall and F1 value, which shows that IFT‐PTransE algorithm can improve the efficiency and quality of video recommendation to a certain extent.…”

Section: Experimental and Results Analysismentioning

confidence: 99%

Research on Personalized Video Matching Algorithm Based on Implicit Feature Transfer and PTransE

Zhang

Wang

et al. 2023

IEEJ Transactions Elec Engng

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With the rapid development of internet plus education, it is increasingly important to quickly and accurately match personalized videos for learners from massive learning videos. However, the existing resource recommendation methods have two problems. On the one hand, they do not make full use of the implicit interaction data of learners in the learning process, i.e., they rarely reflect the comprehensive consideration of learners' interest preferences and cognitive level when describing learners. On the other hand, they are mostly based on collaborative filtering algorithms, using the similarity between learners or videos to recommend, ignoring the impact of semantic relations between videos on the recommendation results. In view of this, we proposed a personalized learning video matching method (IFT-PTransE) based on heterogeneous feature data transfer and knowledge reasoning from two aspects of learning behavior data mining and knowledge graph representation learning. In this method, firstly, a learner model is constructed. By analyzing and quantifying various implicit interaction behavior data of learners, the sparse video scoring matrix is filled as auxiliary data to complete the transfer of target scoring data. Secondly, the semantic close relationship between videos is introduced. PTransE algorithm is used to mine the multi-path relationship between entities. All entity relationships are embedded into the low dimensional vector space, so that the semantic similarity between videos can be calculated using the distance between vectors. Finally, video score similarity and semantic similarity between frequencies are fused. video sorting is performed based on the improved collaborative filtering algorithm and then recommend the top N videos to the students. Through simulation and analysis, the effectiveness of this method in personalized video matching is proved. This method makes up for the shortcomings of collaborative filtering algorithm in using implicit information, enhances the recommendation effect at the semantic level, and solves the data sparse and cold start problems to a certain extent.

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Section: Experimental and Results Analysismentioning

confidence: 99%

Research on Personalized Video Matching Algorithm Based on Implicit Feature Transfer and PTransE

Zhang

Wang

et al. 2023

IEEJ Transactions Elec Engng

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“…A study [87] proposed CTransR-CF algorithm based on knowledge graph embedding, which is fused in collaboration filtering technique to recommend Top-N predictions. They used movie-lens knowledge graph data set provided by "The Movie Database (TMDb)" website 3 .…”

Section: ) Auto Encodersmentioning

confidence: 99%

A Review of Text-Based Recommendation Systems

et al. 2021

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Many websites over the Internet are producing a variety of textual data; such as news, research articles, ebooks, personal blogs, and user reviews. In these websites, the textual data is so large that the process of finding pertinent information by a user often becomes cumbersome. To overcome this issue, "Text-based Recommendation Systems (RS)" are being developed. They are the systems with the capability to find the relevant information in a minimal time using text as the primary feature. There exist several techniques to build and evaluate such systems. And though a good number of surveys compile the general attributes of recommendation systems, there is still a lack of comprehensive literature review about the text-based recommendation systems. In this paper, we present a review of the latest studies on text-based RS. We have conducted this survey by collecting literature from preeminent digital repositories, that was published during the period 2010-2020. This survey mainly covers the four major aspects of the textual based recommendation systems used in the reviewed literature. The aspects are datasets, feature extraction techniques, computational approaches, and evaluation metrics. As benchmark datasets carry a vital role in any research, publicly available datasets are extensively reviewed in this paper. Moreover, for text-based RS many proprietary datasets are also used, which are not available in the public. But we have consolidated all the attributes of these publically available and proprietary datasets to familiarize these attributes to new researchers. Furthermore, the feature extraction methods from the text are briefed and their usage in the construction of text-based RS are discussed. Later, various computational approaches that use these features are also discussed. To evaluate these systems, some evaluation metrics are adopted. We have presented an overview of these evaluation metrics and diagramed them according to their popularity. The survey concludes that Word Embedding is the widely used feature selection technique in the latest research. The survey also deduces that hybridization of text features with other features enhance the recommendation accuracy. The study highlights the fact that most of the work is on English textual data, and News recommendation is the most popular domain.

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“…Alhijawi et al (2018) identified semantic neighbors and satisfaction neighbors based on semantic similarity and satisfaction similarity and then combined them to obtain a neighbor list and produce recommendations. Yang and Guiyun (2020) and Zhu et al (2019) calculated semantic similarity based on a knowledge graph, obtained semantic neighbors Top- N and then integrated it with CF neighbors Top- N to obtain the final recommendation.…”

Section: Related Workmentioning

confidence: 99%

A folksonomy-based collaborative filtering method for crowdsourcing knowledge-sharing communities

Zhou

Yang

et al. 2021

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PurposeThis paper proposes a recommendation method that mines the semantic relationship between resources and combine it with collaborative filtering (CF) algorithm for crowdsourcing knowledge-sharing communities.Design/methodology/approachFirst, structured tag trees are constructed based on tag co-occurrence to overcome the tags' lack of semantic structure. Then, the semantic similarity between tags is determined based on tag co-occurrence and the tag-tree structure, and the semantic similarity between resources is calculated based on the semantic similarity of the tags. Finally, the user-resource evaluation matrix is filled based on the resource semantic similarity, and the user-based CF is used to predict the user's evaluation of the resources.FindingsFolksonomy is a knowledge classification method that is suitable for crowdsourcing knowledge-sharing communities. The semantic similarity between resources can be obtained according to the tags in the folksonomy system, which can be used to alleviate the data sparsity and cold-start problems of CF. Experimental results show that compared with other algorithms, the algorithm in this paper performs better in mean absolute error (MAE) and F1, which indicates that the proposed algorithm yields better performance.Originality/valueThe proposed folksonomy-based CF method can help users in crowdsourcing knowledge-sharing communities to better find the resources they need.

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Top-N Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph Embedding

Cited by 13 publications

References 7 publications

Research on Personalized Video Matching Algorithm Based on Implicit Feature Transfer and PTransE

Research on Personalized Video Matching Algorithm Based on Implicit Feature Transfer and PTransE

A Review of Text-Based Recommendation Systems

A folksonomy-based collaborative filtering method for crowdsourcing knowledge-sharing communities

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