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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 10:50, 21 February 2020
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| Knowledge Graph for Recommendation System | | Knowledge Graph for Recommendation System | ||
| 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). 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. | ||
| [Shichang Ding,sding@gwdg.de] | | [Shichang Ding,sding@gwdg.de] | ||
| [https://www.google.com/search?q=kgat&oq=kgat&aqs=chrome..69i57j0l7.791j0j4&sourceid=chrome&ie=UTF-8] | | [https://www.google.com/search?q=kgat&oq=kgat&aqs=chrome..69i57j0l7.791j0j4&sourceid=chrome&ie=UTF-8] | ||
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| [Bangbang Ren, bangbang.ren@cs.uni-goettingen.de] | | [Bangbang Ren, bangbang.ren@cs.uni-goettingen.de] | ||
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| Wireless Moving Video Surveillance System | |||
| Artificial Intelligence has been and is going to be popular for many years. Static object detection, recognition technique has been studied for many years. However, how these techniques work in a dynamic environment (eg. Self-driving ) is not clear. In this topic, we want to reveal which kind of technique performs better in a video surveillance system with limited computing and network resources. Based on this, our goal is to develop a real Wireless Moving Video Surveillance System which including video analysis, wireless data delivery, and data compression and fusion. Fortunately, we already have some preliminary work. | |||
| Interested in this topic, willing to follow the advisor's guidance, patience and time for reading multiple papers. Interested in embedded development, we will use Raspberry Pi and NVIDIA Jetson Nano Developer Kit. Have Fun With This Project! | |||
| [Weijun Wang, weijun.wang@cs.uni-goettingen.de] | |||
| [https://www.microsoft.com/en-us/research/wp-content/uploads/2017/08/Bahl-MobiCom-2015.pdf] | |||
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