A tag-based recommender system framework for social bookmarking websites
Haibo Liu · International Journal of Web Based Communities · 2018
In social bookmarking websites, social tags contain rich information about individual preference in web resources. Nevertheless, the unsupervised way of tag creation makes the expressions of user's interests are troubled by tag semantic gap. Additionally, in social network sites, the user's interests are influenced by his/her friends' preferences. To handle the problem of personalised interest expression and to recommend the relevant web resource for the users, we propose a tag-based recommender system framework for social bookmarking websites, in which user, tag and resource profiles are expressed reciprocally in a unified form and the 'following interest' is defined based on social network analysis for computing the influence of social relationship on individual interests. We compare our method with several collaborative filtering-based recommendation methods using datasets collected from two social bookmarking websites. The results show that it improves the performance of resource recommendation and outperforms the baseline methods.