Incorporating Semantic Item Representations to Soften the Cold Start Problem

D'Addio, Rafael Martins; Fressato, Eduardo Pereira; Costa, Arthur F. da; Manzato, Marcelo G.

doi:10.1145/3243082.3243112

Cited by 3 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gohari and Tarokh (2016) got the semantic similarity between users and resources based on ontology and predicted the evaluation of resources that users have not evaluated based on those semantic similarities. D'Addio et al (2018) developed resource feature vectors based on sentiment analysis, semantic embedding and semantic similarity and then applied CF based on these vectors. Hong and Yu (2019) proposed an similarity-term frequency (S-TF) method to calculate the importance of tags and combined it with the semantic similarity of tags to calculate the similarity of resources.…”

Section: Related Workmentioning

confidence: 99%

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

Zhou

Yang

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%