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Seminar on Internet Technologies (Summer 2020): Difference between revisions
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Seminar on Internet Technologies (Summer 2020) (view source)
Revision as of 13:32, 14 April 2020
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|{{Hl2}} |'''Initial Readings''' | |{{Hl2}} |'''Initial Readings''' | ||
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| Knowledge Graph for Recommendation System | | Knowledge Graph for Recommendation System(Akshay Katyal) | ||
| The success of the 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). Despite its effectiveness and universality, CF methods suffer from the inability of modeling side information, 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. | | The success of the 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). Despite its effectiveness and universality, CF methods suffer from the inability of modeling side information, 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. | ||
| Have basic knowledge for deep learning. Interested in this topic, patience and time for reading and concluding multiple papers. | | Have basic knowledge for deep learning. Interested in this topic, patience and time for reading and concluding multiple papers. | ||