Folksonomy-Based Recommender Systems: A State-of-the-Art Review

Godoy, Daniela; Corbellini, Alejandro

doi:10.1002/int.21753

Cited by 34 publications

(19 citation statements)

References 104 publications

(143 reference statements)

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“…There are two methods to implement the KG representation learning: (1) tensor decomposition-based methods and (2) translation-based methods. The former includes NTN [15][16][17][18][19] and RESCAL [19][20][21][22]. The latter includes TransE [23][24][25], TransH [26][27][28][29], and TransR/CtransR [30][31][32].…”

Section: Knowledge Graph Representationmentioning

confidence: 99%

Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph

Zeng

2018

Mathematical Problems in Engineering

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To solve the problem that collaborative filtering algorithm only uses the user-item rating matrix and does not consider semantic information, we proposed a novel collaborative filtering recommendation algorithm based on knowledge graph. Using the knowledge graph representation learning method, this method embeds the existing semantic data into a low-dimensional vector space. It integrates the semantic information of items into the collaborative filtering recommendation by calculating the semantic similarity between items. The shortcoming of collaborative filtering algorithm which does not consider the semantic information of items is overcome, and therefore the effect of collaborative filtering recommendation is improved on the semantic level. Experimental results show that the proposed algorithm can get higher values on precision, recall, and F-measure for collaborative filtering recommendation.

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Section: Knowledge Graph Representationmentioning

confidence: 99%

Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph

Zeng

2018

Mathematical Problems in Engineering

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“…Along with the basic FolkRank, numerous researches are conducted on improving FolkRank [8,22,39,55]. Detailed information of the studies on collaborative tagging systems, also known as folksonomie can be found in a review paper [9]. Another survey paper compared the performance of different approaches of tagging systems focusing on adding semantics in folksonomies [18].…”

Section: Tag Recommendationmentioning

confidence: 99%

User-Aware Folk Popularity Rank

Wang

Zhang

Yamasaki

2019

Proceedings of the 27th ACM International Conference on Multimedia

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In this paper we propose a method that can enhance the social popularity of a post (i.e., the number of views or likes) by recommending appropriate hash tags considering both content popularity and user popularity. A previous approach called FolkPopularityRank (FP-Rank) considered only the relationship among images, tags, and their popularity. However, the popularity of an image/video is strongly affected by who uploaded it. Therefore, we develop an algorithm that can incorporate user popularity and users' tag usage tendency into the FP-Rank algorithm. The experimental results using 60,000 training images with their accompanying tags and 1,000 test data, which were actually uploaded to a real social network service (SNS), show that, in ten days, our proposed algorithm can achieve 1.2 times more views than the FP-Rank algorithm. This technology would be critical to individual users and companies/brands who want to promote themselves in SNSs.

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“…The collaborative filtering technology is generally divided into the method based on neighborhood and the method based on model [1]. The collaborative tagging is also called folksonomy [3], with which users can tag favorite resources freely for subsequent management and searching. Users describe resources with tags, so tags are the ties between users and resources.…”

Section: Related Workmentioning

confidence: 99%

A Tag-based Recommendation Algorithm Integrating Short-term and Long-term Interests of Users

Li¹,

Zhang²

2017

dtcse

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Abstract. In the collaborative tagging system, tags of users contain rich information on personalized preference, and time stamps of users show their interest changes. Users' interest has the varied timeliness, but the existing recommendation algorithm fusing time information only emphasizes the short-term interest of users but fails to dig into the long-term stable interest of users and thus presents the low recommendation precision. In this paper, we propose a tag-based recommendation algorithm integrating short-term and long-term interests of users. The algorithm first builds the short-term and long-term interest characteristics of users based on tags according to the use frequency, time decay, life cycle and volatility; then it maps the characteristics to the resources tagged by the user to form a user-resource pseudo-scoring matrix. Finally, it calculates the set of the nearest neighbors of user with the matrix to give recommendations. The verification on the delicious data set shows that the algorithm improves recommendation precision and diversity compared with other classical algorithms.

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Folksonomy-Based Recommender Systems: A State-of-the-Art Review

Cited by 34 publications

References 104 publications

Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph

Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph

User-Aware Folk Popularity Rank

A Tag-based Recommendation Algorithm Integrating Short-term and Long-term Interests of Users

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