Seminar on Internet Technologies (Summer 2020): Difference between revisions

| The success of recommendation system makes it prevalent in Web applications, ranging from search engines, E-commerce, to social media sites and news portals.To predict user preference from the key (and widely available) source of user behavior data, much research effort has been devoted to collaborative filtering (CF) [12, 13, 32]. Despite its effectiveness and universality, CF methods suffer from the inability of modeling side information [30, 31], such as item attributes, user profiles, and contexts, thus perform poorly in sparse situations where users and items have few interactions.To address the limitation of CF models, a solution is to take the graph of item side information, aka. knowledge graph into account to construct the predictive model.