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Seminar on Internet Technologies (Winter 2018/2019): Difference between revisions
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Seminar on Internet Technologies (Winter 2018/2019) (view source)
Revision as of 10:33, 26 October 2018
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| A large number of new buyers are often acquired by merchants during promotions. However, many of the attracted buyers are one-time deal hunters, and the promotions may have little long-lasting impact on sales. It is important for merchants to identify who can be converted to regular loyal buyers and then target them to reduce promotion cost and increase the return on investment (ROI). Our goal in this topic is to do a survey about the key factors leading to successful purchasing actions. On the basis, further work such as purchasing prediction and personalized recommendation can be carried out. | | A large number of new buyers are often acquired by merchants during promotions. However, many of the attracted buyers are one-time deal hunters, and the promotions may have little long-lasting impact on sales. It is important for merchants to identify who can be converted to regular loyal buyers and then target them to reduce promotion cost and increase the return on investment (ROI). Our goal in this topic is to do a survey about the key factors leading to successful purchasing actions. On the basis, further work such as purchasing prediction and personalized recommendation can be carried out. | ||
| Basic machine learning knowledge | | Basic machine learning knowledge | ||
|[ | |[Bo Zhao--<bo.zhao@gwdg.de>] | ||
| [https://dl.acm.org/citation.cfm?id=2939674][https://dl.acm.org/citation.cfm?id=3219826] | | [https://dl.acm.org/citation.cfm?id=2939674][https://dl.acm.org/citation.cfm?id=3219826] | ||
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| '''Recommender systems for E-commerce''' | |||
|The proliferation of mobile devices especially smart phones brings remarkable opportunities for the e-commerce development. Recommendation systems have been being widely used by almost all the e-commerce platforms to help consumers find their ideal products to purchase more quickly. Modern recommendation systems have become increasingly more complex compared to their early content-based and collaborative filtering versions. In this survey, we will cover recent advances in recommendation methods, focusing on matrix factorization, multi-armed bandits, and methods for blending recommendations. We will also describe evaluation techniques, and outline open issues and challenges. The ultimate goal of this tutorial is to present a toolkit of new recommendation methods in perspective to data-related problems, and highlight opportunities and new research paths for researchers and practitioners that work on problems in the intersection of recommendation systems and databases. | |||
|Basic knowledge about recommender system and machine learning | |||
[Bo Zhao--<bo.zhao@gwdg.de>] | |||
| [https://dl.acm.org/citation.cfm?id=2789995][https://arxiv.org/abs/1801.02294] | |||
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| '''Getting a Practical Understanding of Segment Routing''' | | '''Getting a Practical Understanding of Segment Routing''' | ||