Using Tags for Measuring the Semantic Similarity of Users to Enhance Collaborative Filtering Recommender Systems

Abstract

Recent years have seen a significant growth in social tagging systems, which allow users to use their own generated tags to organize, categorize, describe and search digital content on social media. The growing popularity of tagging systems is leading to an increasing need for automatic generation of recommended items for users. Much previous research focuses on incorporating recommender techniques in social tagging systems to support the suggestion of suitable tags for annotating related items. Collaborative filtering is one such technique. The most critical task in collaborative filtering is finding related users with similar preferences, i.e., "liked-minded" users. Despite the popularity of collaborative filtering, it still suffers from certain limitations in relation to "cold-start" users, for example, where often there are insufficient preferences to make recommendations. Moreover, there is the data-sparsity problem, where there is limited user feedback data to identify similarities in users' interests because there is no intersection between users' transactional data a situation which also results in degraded recommendation quality. For this reason, in this paper, we present a new collaborative filtering approach based on users' semantic tags, which calculates the similarity between users by discovering the semantic spaces in their posted tags. We believe that this approach better reflects the semantic similarity between users according to their tagging perspectives and consequently improves recommendations through the identification of semantically related items for each user. Our experiment on a real-life dataset shows that the proposed approach outperforms the traditional user-based collaborative filtering approach in terms of improving the quality of recommendations.

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

“…Movahedian and Khayyambashi (2014) filtered tags to construct the semantic profiles of users and resources and then identified the semantic relationship of users and resources based on these profiles and external ontology. Ghabayen and Noah (2017) mapped tags to Wordnet to calculate the semantic similarity between tags. The semantic similarity between users is calculated based on the semantic similarity between tags and users' labeling records.…”

Contextual Sentiment Based Recommender System to Provide Recommendation in the Electronic Products Domain

“…This may result in the data sparsity problem. As a result, few research works have been done to overcome the above-mentioned problem [18].…”

Towards Serendipity for Content–Based Recommender Systems

“…Another features such as tags [3], can also be used as a basic keyword-based approach for item representation especially in conventional content-based recommender systems. These features are yet to be explored in identifying serendipitous items for content-based recommendations.…”

“…Recommender system technology is one of the solutions to overcome the problem of information overload. The technology has been applied to various applications such as information retrieval systems [3], on-line learning [4,5] and planning [6]. Recommender systems guide users based on their feedback, in which it can be obtained explicitly from ratings or implicitly through user actions on the web (item purchase/item view) or via users' past purchasing or interactivity experiences when using the system.…”