Personalization in tag ontology learning for recommendation making

Abstract

Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. One of the most popular web personalization systems is recommender systems. In recommender systems choosing user information that can be used to profile users is very crucial for user profiling. In Web 2.0, one facility that can help users organize Web resources of their interest is user tagging systems. Exploring user tagging behavior provides a promising way for understanding users' information needs since tags are given directly by users. However, free and relatively uncontrolled vocabulary makes the user self-defined tags lack of standardization and semantic ambiguity. Also, the relationships among tags need to be explored since there are rich relationships among tags which could provide valuable information for us to better understand users. In this paper, we propose a novel approach for learning tag ontology based on the widely used lexical database WordNet for capturing the semantics and the structural relationships of tags. We present personalization strategies to disambiguate the semantics of tags by combining the opinion of WordNet lexicographers and users' tagging behavior together. To personalize further, clustering of users is performed to generate a more accurate ontology for a particular group of users. In order to evaluate the usefulness of the tag ontology, we use the tag ontology in a pilot tag recommendation experiment for improving the recommendation performance by exploiting the semantic information in the tag ontology. The initial result shows that the personalized information has improved the accuracy of the tag recommendation.

Building Domain Ontologies Out of Folksonomies and Linked Data

“…[29][30][31][32][33] Some of these approaches rely on specific techniques such as clustering, 21 social networks metrics, 23 formal concept analysis, 4 mappings between tags and lexical resources 30 or ontologies, 31 while others use a combination of them. 14,34 For an extensive review of the state of the art regarding folksonomies as source of knowledge we refer the reader to Limpens et al, 35 and more recently García-Silva.…”

“…r to find the ontological concepts. Similar to FLOR, Djuana et al 32 also use Wordnet in order to capture both the meaning of a tag and its structural relation to other tags, particularly is-a and part-of relations. Different from the others, Alves and Santanché 33 use domain ontologies rather that wide purpose ontologies such as Wordnet.…”

A Review Selection Method Using Product Feature Taxonomy

“…Based upon the relations, a hierarchical structure between tags is constructed. [9] proposed to construct tag ontology from folksonomy based on WordNet and also personalized the tag ontology based on user clusters. [10] presented an approach to construct a hierarchical product profile which contains product features and relationships between them.…”

“…Researchers made a lot of efforts to find the relationship between different terms or concepts more effectively and accurately. By making use of various techniques such as text mining and ontology learning, people now are able to generate product ontology or taxonomy about product features and relationships between features from data about products or even from user generated information such as tags and review text [8][9][10]. In this paper, we introduce a review selection method called RMS (Review Model based review selection).…”

Ontology Mapping and Reasoning in Semantic Time Series Processing

“…The authors argue that additional triples, which are generated in RDF(S) processing cause poor performance and this is the point where this problem can be tackled. [5] propose an approach for learning a tag ontology based on WordNet for capturing the semantics and the structural relationships of tags. [16] propose a method for generation of correspondences between ontologies.…”