智能決策與機(jī)器學(xué)習(xí)研究中心系列講座（三） 2019-12-18

講座題目：When to and When Not To Make a Recommendation? A Product-Centric Approach to Optimize the Timing Decision of Online Recommendations

報告人：常象宇 bwin必贏唯一官網(wǎng)副教授

報告時間：2019年12月23日星期一下午15：00-16：30

報告地點：管院311會議室

報告內(nèi)容: Recommending the right product at the right time for consumers is the goal of modern online recommendation systems. However, consumers who shop in e-commerce often complain about a phenomenon that the product which you just bought is recommended again. There are two possibilities for this kind of complaint. First, the recommended items do not need to be purchased repeatedly. Second, the recommended item is repurchable, but the recommendation time is not proper. To optimize current recommendation systems and improve consumer’s satisfactions, this paper studies the following problems:

1. Why recommend products that you just purchased?

2. What type of products are prone to repurchase? What type of products are not prone to repurchase?

3. What is an optimal time interval for the recommendation?

4. Can we optimize the current recommendation system so that we can reduce customer complaints while still keep the purchase rate?

To this end, we introduce the consumer-based analysis which has been well studied in the marketing science into the machine-learning-based recommendation systems to handle the above problems.

報告人簡介：常象宇， bwin必贏唯一官網(wǎng)副教授，華盛頓大學(xué)西雅圖分校工業(yè)與系統(tǒng)工程系客座副教授。2017年入選陜西省高等學(xué)校優(yōu)秀青年學(xué)者支持計劃。研究主要集中在統(tǒng)計機(jī)器學(xué)習(xí)，及其在管理問題上的應(yīng)用。曾在統(tǒng)計學(xué)期刊AOS，JOE，SS，EJS等；機(jī)器學(xué)習(xí)期刊JMLR，TNNLS，TC，TSP等發(fā)表論文三十余篇。同時也曾是人工智能與數(shù)據(jù)挖掘相關(guān)會議：ICML，AAAI，IJCAI，SDM，ICDM，ICDS 等的程序委員會委員。