The Seoul Check of Time Award “is awarded yearly to the writer or authors of a paper offered at a earlier World Broad Net convention that has, because the identify suggests, stood the check of time.”
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In 2017, when the journal IEEE Web Computing was celebrating its twentieth anniversary, its editorial board determined to determine the only paper from its publication historical past that had greatest withstood the “check of time”. The respect went to a 2003 paper known as “Amazon.com Suggestions: Merchandise-to-Merchandise Collaborative Filtering”, by then Amazon researchers Greg Linden, Brent Smith, and Jeremy York.
“The paper tackles an vital downside from a novel angle that turned out to be one of many basic strategies within the years to return after publication,” stated Li. “The paper considers suggestion as a reinforcement studying downside, which was not a preferred view at the moment.”
Li and his colleagues, who labored at Yahoo! Labs in 2010, launched a brand new mind-set about customized suggestion engines. The workforce addressed the problem of making a personalised suggestion engine to instantly maximize a utility perform that measures consumer satisfaction.
Recommender methods on the time relied on previous consumer actions to supply significant suggestions at a person stage. Nevertheless, the paper notes, “in lots of web-based situations, the content material universe undergoes frequent modifications, with content material reputation altering over time as properly. Moreover, there are new guests to an internet site with no historic consumption document.”
“These points make conventional recommender-system approaches troublesome to use,” the paper states. “It thus turns into indispensable to study the goodness of match between consumer pursuits and content material from consumer interactions, when one or each of them are new.”
Contextual bandits
The paper proposed a contextual-bandit strategy to driving customized suggestions in information content material “during which a studying algorithm sequentially selects articles to serve customers primarily based on contextual details about the customers and articles, whereas concurrently adapting its article-selection technique primarily based on user-click suggestions to maximise whole consumer clicks.”
“Information content material modifications each hour inside the day,” stated Li. “That’s why we want an answer to rapidly adapt to altering content material, and advocate one of the best content material to customers.” In doing so, the answer has to stability two competing targets: maximizing consumer satisfaction and gathering details about “goodness of match” between consumer curiosity and content material. Contextual bandits are a particular class of reinforcement studying issues which are well-suited to the situation.
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Functions in product suggestion and natural-language processing exhibit the strategy’s flexibility and ease of use.
The paper develops sensible contextual bandit algorithms, which optimize metrics about consumer engagement comparable to click-through charges, downstream income, or different enterprise impacts. Li later labored on extending his strategy to situations during which utility is measured when it comes to long-term consumer engagements.
“In actuality, choices change the habits of the consumer and, in flip, change the longer term manner they work together with the web site and the longer term utility,” stated Li. “So a system ought to have the ability to take these long-term impacts into consideration and decide to maximise long-term utility as a substitute of short-term.”
The authors reported that their “computationally environment friendly contextual bandit algorithm” not solely drove greater click-through charges but in addition solved for the scaling problem as a result of it might be “reliably evaluated offline utilizing beforehand recorded random visitors.” The analysis approach itself has additionally discovered makes use of in different web-based situations.
The trail to the prize
Li obtained a bachelor of engineering in laptop science and expertise at Tsinghua College in Beijing, then went on to earn a grasp of science in computing science on the College of Alberta. He earned his PhD in laptop science from Rutgers College, working within the space of reinforcement studying.
Throughout his time at Rutgers, Li met two mentors who would later grow to be coauthors on the award-winning paper. Schapire was a Princeton professor on Li’s thesis protection committee, and Langford was Li’s internship mentor at Yahoo! in 2007. In October 2020, Li joined Amazon as a senior principal scientist.
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“One factor that attracted me is the client obsession tradition of Amazon that makes use of stable science applied sciences and options to deal with deep buyer questions,” Li stated. “Contextual bandits and, extra typically, reinforcement studying strategies may help Amazon fulfill buyer wants in purchasing, leisure, and past, in addition to play a key position in bettering massive language fashions.”
Li and his colleagues obtained the Seoul Check of Time Award on the Net Convention 2023 in Austin, Texas.
“I used to be thrilled, and profitable was completely surprising,” stated Li.
First conceived in 1989 by Tim Berners-Lee at CERN in Geneva, the Net Convention (previously often known as the Worldwide World Broad Net Convention, abbreviated as WWW) is a yearly worldwide tutorial convention on the subject of the longer term instructions of the World Broad Net.
“Scientists typically publish innovation in papers. When the invention stays on paper and doesn’t attain the actual world, it doesn’t really feel just like the story is full,” Li stated. “This award is a recognition that the invention has had a long-lasting influence, not simply on the issue we labored on, but in addition within the area and in different elements of the business. I’m grateful to be a recipient of the award and am gratified to see that this 13-year-old work continues to be helpful.”
