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nb nc mu mv paragraph-image"><div class="mu mv mw"><picture><source srcSet="https://miro.medium.com/v2/resize:fit:640/format:webp/1*amaIijWUqSqpELilnvKO5g.png 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/1*amaIijWUqSqpELilnvKO5g.png 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/1*amaIijWUqSqpELilnvKO5g.png 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/1*amaIijWUqSqpELilnvKO5g.png 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/1*amaIijWUqSqpELilnvKO5g.png 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/1*amaIijWUqSqpELilnvKO5g.png 1100w, https://miro.medium.com/v2/resize:fit:1390/format:webp/1*amaIijWUqSqpELilnvKO5g.png 1390w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 695px" type="image/webp"/><source data-testid="og" srcSet="https://miro.medium.com/v2/resize:fit:640/1*amaIijWUqSqpELilnvKO5g.png 640w, https://miro.medium.com/v2/resize:fit:720/1*amaIijWUqSqpELilnvKO5g.png 720w, https://miro.medium.com/v2/resize:fit:750/1*amaIijWUqSqpELilnvKO5g.png 750w, https://miro.medium.com/v2/resize:fit:786/1*amaIijWUqSqpELilnvKO5g.png 786w, https://miro.medium.com/v2/resize:fit:828/1*amaIijWUqSqpELilnvKO5g.png 828w, https://miro.medium.com/v2/resize:fit:1100/1*amaIijWUqSqpELilnvKO5g.png 1100w, https://miro.medium.com/v2/resize:fit:1390/1*amaIijWUqSqpELilnvKO5g.png 1390w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 695px"/><img alt="" class="bh mb nd c" width="695" height="876" loading="eager" role="presentation"/></picture></div></figure><h1 id="c568" class="ne nf gu bf ng nh ni nj nk nl nm nn no np nq nr ns nt nu nv nw nx ny nz oa ob bk">Introduction: Sharing Insights and Best Practices</h1><p id="6420" class="pw-post-body-paragraph oc od gu oe b of og oh oi oj ok ol om on oo op oq or os ot ou ov ow ox oy oz gn bk">Working closely with the broader recommender community, including innovative global leaders like <a class="af pa" href="https://www.tencent.com/en-us/about.html" rel="noopener ugc nofollow" target="_blank">Tencent</a>, enables NVIDIA to incorporate best practices, insights, and learnings back into our<a class="af pa" href="https://nvda.ws/3zGP4wI" rel="noopener ugc nofollow" target="_blank"> recommender framework</a>. At NVIDIA, we are committed to streamlining the building, deploying, and optimizing of recommender systems. Our engineering teams participate in <a class="af pa" href="https://nvda.ws/3kDEvEp" rel="noopener ugc nofollow" target="_blank">industry challenges</a>, <a class="af pa" href="https://nvda.ws/2WAioqs" rel="noopener ugc nofollow" target="_blank">host community events</a>, and work closely with early adopters like Tencent. Xiangting Kong, Expert Engineer, leads the design and development of Tencent’s Advertising and Deep Learning Platform. While Kong presented at<a class="af pa" href="https://nvda.ws/2WCRtdv" rel="noopener ugc nofollow" target="_blank"> GTC Spring</a>, we asked him to share some additional insights on building recommender systems in this interview.</p><h1 id="35f3" class="ne nf gu bf ng nh ni nj nk nl nm nn no np nq nr ns nt nu nv nw nx ny nz oa ob bk">Interview with Xiangting Kong, Expert Engineer, Tencent</h1><p id="8be6" class="pw-post-body-paragraph oc od gu oe b of og oh oi oj ok ol om on oo op oq or os ot ou ov ow ox oy oz gn bk"><strong class="oe gv">Question: What is your role at Tencent?</strong></p><p id="111c" class="pw-post-body-paragraph oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz gn bk"><strong class="oe gv"><em class="pg">Xiangting Kong: </em></strong><em class="pg">I am an expert engineer at Tencent and am responsible for the design and development of the advertising recommendation system. I am also the lead of the Tencent Advertising and Deep Learning Platform. Our platform supports the machine learning model optimization, training, and inference in a variety of business scenarios from advertising and fintech to networking data mining.</em></p><p id="4147" class="pw-post-body-paragraph oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz gn bk"><strong class="oe gv">Questions: What does your team at Tencent work on?</strong></p><p id="0930" class="pw-post-body-paragraph oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz gn bk"><strong class="oe gv"><em class="pg">Xiangting Kong: </em></strong><em class="pg">Our team mainly develops machine learning platforms and we are responsible for feature engineering, model training and online inference. We are working on implementing a new generation of high-performance distributed training system for advertising recommendation based on GPU from 0 to 1.</em></p><p id="e5ad" class="pw-post-body-paragraph oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz gn bk"><strong class="oe gv">Question: How does your work and your team’s work on recommenders relate to Tencent’s overall business?</strong></p><p id="c7e2" class="pw-post-body-paragraph oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz gn bk"><strong class="oe gv"><em class="pg">Xiangting Kong: </em></strong><em class="pg">Our advertising recommendation training platform covers the entire Tencent traffic business. Tencent advertising recommendations are widely used in services such as WeChat, Moments, QQ, Tencent Games, Tencent Video, Tencent News and so on. Tencent advertising revenue is in the hundreds of millions. The accuracy of our advertising recommendation helps increase advertising revenue.</em></p><figure class="pi pj pk pl pm nc mu mv paragraph-image"><div role="button" tabindex="0" class="pn po fj pp bh pq"><div class="mu mv ph"><picture><source srcSet="https://miro.medium.com/v2/resize:fit:640/format:webp/0*DspuOfOv0e_it3BB 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/0*DspuOfOv0e_it3BB 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/0*DspuOfOv0e_it3BB 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/0*DspuOfOv0e_it3BB 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/0*DspuOfOv0e_it3BB 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/0*DspuOfOv0e_it3BB 1100w, https://miro.medium.com/v2/resize:fit:1400/format:webp/0*DspuOfOv0e_it3BB 1400w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 700px" type="image/webp"/><source data-testid="og" srcSet="https://miro.medium.com/v2/resize:fit:640/0*DspuOfOv0e_it3BB 640w, https://miro.medium.com/v2/resize:fit:720/0*DspuOfOv0e_it3BB 720w, https://miro.medium.com/v2/resize:fit:750/0*DspuOfOv0e_it3BB 750w, https://miro.medium.com/v2/resize:fit:786/0*DspuOfOv0e_it3BB 786w, https://miro.medium.com/v2/resize:fit:828/0*DspuOfOv0e_it3BB 828w, https://miro.medium.com/v2/resize:fit:1100/0*DspuOfOv0e_it3BB 1100w, https://miro.medium.com/v2/resize:fit:1400/0*DspuOfOv0e_it3BB 1400w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 700px"/><img alt="Our advertising recommendation training platform covers the entire Tencent traffic business. Tencent advertising recommendations are widely used in services such as WeChat, Moments, QQ, Tencent Games, Tencent Video, Tencent News and so on. Tencent advertising revenue is in the hundreds of millions. The accuracy of our advertising recommendation helps increase advertising revenue." class="bh mb nd c" width="700" height="392" loading="lazy"/></picture></div></div><figcaption class="pr ff ps mu mv pt pu bf b bg z du"><em class="pv">From Xiangting Kong’s GTC Spring 2021 Presentation</em></figcaption></figure><p id="d456" class="pw-post-body-paragraph oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz gn bk"><strong class="oe gv">Question: Is your team a relatively new team? Why did Tencent decide to invest in recommenders?</strong></p><p id="96d4" class="pw-post-body-paragraph oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz gn bk"><strong class="oe gv"><em class="pg">Xiangting Kong: </em></strong><em class="pg">Our team has been established for years. The advertising business is a relatively important business inside Tencent and the recommendation system is used to increase the overall advertising revenue.</em></p><p id="2400" class="pw-post-body-paragraph oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz gn bk"><strong class="oe gv">Question: What kind of recommender systems does your team focus on?</strong></p><p id="42b3" class="pw-post-body-paragraph oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz gn bk"><strong class="oe gv"><em class="pg">Xiangting Kong: </em></strong><em class="pg">The main focus of our team is the advertising recommendation system, responsible for the optimization of the advertising training platform. Tencent advertising recommendations system consists of parts including offline feature engineering, training platform, online inference system, online feature engineering and play platform. Advertising recommendations is a process of gradual filtering. Sorting stages include recall, pre-ranking, and ranking. Each stage has different requirements. The rapid investigation and iteration of the model puts forth higher requirements for training performance.</em></p><figure class="pi pj pk pl pm nc mu mv paragraph-image"><div role="button" tabindex="0" class="pn po fj pp bh pq"><div class="mu mv pw"><picture><source srcSet="https://miro.medium.com/v2/resize:fit:640/format:webp/0*cagsGZ-N39ox908d 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/0*cagsGZ-N39ox908d 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/0*cagsGZ-N39ox908d 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/0*cagsGZ-N39ox908d 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/0*cagsGZ-N39ox908d 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/0*cagsGZ-N39ox908d 1100w, https://miro.medium.com/v2/resize:fit:1400/format:webp/0*cagsGZ-N39ox908d 1400w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 700px" type="image/webp"/><source data-testid="og" srcSet="https://miro.medium.com/v2/resize:fit:640/0*cagsGZ-N39ox908d 640w, https://miro.medium.com/v2/resize:fit:720/0*cagsGZ-N39ox908d 720w, https://miro.medium.com/v2/resize:fit:750/0*cagsGZ-N39ox908d 750w, https://miro.medium.com/v2/resize:fit:786/0*cagsGZ-N39ox908d 786w, https://miro.medium.com/v2/resize:fit:828/0*cagsGZ-N39ox908d 828w, https://miro.medium.com/v2/resize:fit:1100/0*cagsGZ-N39ox908d 1100w, https://miro.medium.com/v2/resize:fit:1400/0*cagsGZ-N39ox908d 1400w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 700px"/><img alt="The main focus of our team is the advertising recommendation system, responsible for the optimization of the advertising training platform. Tencent advertising recommendations system consists of parts including offline feature engineering, training platform, online inference system, online feature engineering and play platform. Advertising recommendations is a process of gradual filtering. Sorting stages include recall, pre-ranking, and ranking. Each stage has different requirements. The rapid i" class="bh mb nd c" width="700" height="451" loading="lazy"/></picture></div></div><figcaption class="pr ff ps mu mv pt pu bf b bg z du"><em class="pv">From Xiangting Kong’s GTC Spring 2021 Presentation</em></figcaption></figure><p id="80c0" class="pw-post-body-paragraph oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz gn bk"><strong class="oe gv">Question: How does your team conduct training?</strong></p><p id="c46f" class="pw-post-body-paragraph oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz gn bk"><strong class="oe gv"><em class="pg">Xiangting Kong:</em></strong><em class="pg"> We organize some technology sharing every one or two weeks.</em></p><p id="15ed" class="pw-post-body-paragraph oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz gn bk"><strong class="oe gv">Question: How does your team evaluate your recommender systems? fine tune?</strong></p><p id="1ae3" class="pw-post-body-paragraph oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz gn bk"><strong class="oe gv"><em class="pg">Xiangting Kong: </em></strong><em class="pg">Through our recommendation system, we optimize the algorithm strategy, add more samples and features, and assess whether it can drive the increase of income. The accuracy of the advertisement recommendation can be improved by training more sample data, by adding more sample features. But this leads to longer training time and effects the update frequency of the model. In order to ensure that the model updates will not be derailed, the training performance of the model needs to be continuously improved. After the training model performance is improved, more data can be trained to improve the accuracy of the model, thereby increasing the advertising revenue.</em></p><p id="1b10" class="pw-post-body-paragraph oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz gn bk"><strong class="oe gv">Question: How do you optimize your recommender systems? For example, it is our understanding that Tencent uses HugeCTR for embeddings optimization. How has this helped you optimize your workflow?</strong></p><p id="9344" class="pw-post-body-paragraph oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz gn bk"><strong class="oe gv"><em class="pg">Xiangting Kong: </em></strong><em class="pg">HugeCTR, as a recommendation training framework, is integrated into the advertising recommendation training system to make the update frequency of model training faster, and more samples can be trained to improve online effects.</em></p><p id="f60b" class="pw-post-body-paragraph oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz gn bk"><strong class="oe gv">Question: How do you choose the appropriate technique, package, method, or frameworks to support your work?</strong></p><p id="7dfb" class="pw-post-body-paragraph oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz gn bk"><strong class="oe gv"><em class="pg">Xiangting Kong:</em></strong><em class="pg"> The technology or framework we choose must be compatible with the community ecosystem, so that we can do better follow-up upgrades.</em></p><p id="8b8d" class="pw-post-body-paragraph oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz gn bk"><strong class="oe gv">Question: How do you address scaling your models?</strong></p><p id="0a90" class="pw-post-body-paragraph oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz gn bk"><strong class="oe gv"><em class="pg">Xiangting Kong:</em></strong><em class="pg"> Using a larger model is conducive to learning more features, thereby improving the accuracy of the model.</em></p><p id="d16b" class="pw-post-body-paragraph oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz gn bk"><strong class="oe gv">Question: What is a recent success for the team?</strong></p><p id="c4aa" class="pw-post-body-paragraph oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz gn bk"><strong class="oe gv"><em class="pg">Xiangting Kong:</em></strong><em class="pg"> In our training framework, a data-parallel distributed solution has been developed.</em></p><p id="1d53" class="pw-post-body-paragraph oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz gn bk"><strong class="oe gv">Question: Have you recently integrated specific methods into your recommender workflow?</strong></p><p id="b6ea" class="pw-post-body-paragraph oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz gn bk"><strong class="oe gv"><em class="pg">Xiangting Kong:</em></strong><em class="pg"> We recently integrated the CSR [Compressed Sparse Row] pipeline into our ad recommendation training system. CSR type training data is generated so that the data can be directly read on the GPU for training. Through our optimization of the data processing pipeline, the CPU load is greatly reduced and the GPU utilization is greatly improved.</em></p><p id="422f" class="pw-post-body-paragraph oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz gn bk"><strong class="oe gv">Question: If a team lead was just starting out and currently evaluating building, deploying, and optimizing recommenders for their company….what advice would you relay to help them accelerate or streamline their recommender workflows?</strong></p><p id="4fae" class="pw-post-body-paragraph oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz gn bk"><strong class="oe gv"><em class="pg">Xiangting Kong:</em></strong><em class="pg"> Choose a mature technical framework and be compatible with the community ecosystem to facilitate subsequent system upgrades.</em></p><h1 id="8781" class="ne nf gu bf ng nh ni nj nk nl nm nn no np nq nr ns nt nu nv nw nx ny nz oa ob bk">Additional Community Resources to Consider</h1><p id="1f88" class="pw-post-body-paragraph oc od gu oe b of og oh oi oj ok ol om on oo op oq or os ot ou ov ow ox oy oz gn bk">As NVIDIA is committed to streamlining recommender workflows, we incorporate best practices, insights, and learnings from the broader industry, including innovative early adopters like Tencent. Additional resources and events to consider if you are looking for additional best practices to help accelerate recommender workflows:</p><ul class=""><li id="7b57" class="oc od gu oe b of pb oh oi oj pc ol om on pd op oq or pe ot ou ov pf ox oy oz px py pz bk"><a class="af pa" href="https://des.cloud.tencent.com/2021/" rel="noopener ugc nofollow" target="_blank">Tencent Global Digital Conference</a></li><li id="1de0" class="oc od gu oe b of qa oh oi oj qb ol om on qc op oq or qd ot ou ov qe ox oy oz px py pz bk"><a class="af pa" href="https://nvda.ws/2WCRtdv" rel="noopener ugc nofollow" target="_blank">GTC Spring 2021 Session: Learn How Tencent Deployed An Advertising System on Merlin</a></li><li id="08dd" class="oc od gu oe b of qa oh oi oj qb ol om on qc op oq or qd ot ou ov qe ox oy oz px py pz bk"><a class="af pa" href="https://youtu.be/XiwVziNh_3s?t=136" rel="noopener ugc nofollow" target="_blank">GTC Fall Keynote Featuring Tencent in Production with NVIDIA Merlin</a></li><li id="3cc1" class="oc od gu oe b of qa oh oi oj qb ol om on qc op oq or qd ot ou ov qe ox oy oz px py pz bk"><a class="af pa" href="https://nvda.ws/2WAioqs" rel="noopener ugc nofollow" target="_blank">Deep Learning Recommender Systems Summit</a></li></ul></div></div></div></div></section></div></div></article></div><div class="ab cb"><div class="ci bh fz ga gb gc"><div class="qf qg ab ja"><div class="qh ab"><a class="qi ay am ao" rel="noopener follow" href="/tag/deep-learning?source=post_page-----37f1eed898a7---------------------------------------"><div class="qj fj cx qk ge ql qm bf b bg z bk qn">Deep Learning</div></a></div><div class="qh ab"><a class="qi ay am ao" rel="noopener follow" href="/tag/recommender-systems?source=post_page-----37f1eed898a7---------------------------------------"><div class="qj fj cx qk ge ql qm bf b bg z bk qn">Recommender Systems</div></a></div><div class="qh ab"><a class="qi ay am ao" rel="noopener follow" 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Tencent advertising recommendations are widely used in services such as WeChat, Moments, QQ, Tencent Games, Tencent Video, Tencent News and so on. Tencent advertising revenue is in the hundreds of millions. The accuracy of our advertising recommendation helps increase advertising revenue."},"Paragraph:8a641d19e810_11":{"__typename":"Paragraph","id":"8a641d19e810_11","name":"4b1c","type":"IMG","href":null,"layout":"INSET_CENTER","metadata":{"__ref":"ImageMetadata:0*DspuOfOv0e_it3BB"},"text":"From Xiangting Kong’s GTC Spring 2021 Presentation","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"EM","start":0,"end":50,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:8a641d19e810_12":{"__typename":"Paragraph","id":"8a641d19e810_12","name":"d456","type":"P","href":null,"layout":null,"metadata":null,"text":"Question: Is your team a relatively new team? 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The advertising business is a relatively important business inside Tencent and the recommendation system is used to increase the overall advertising revenue.","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"STRONG","start":0,"end":16,"href":null,"anchorType":null,"userId":null,"linkMetadata":null},{"__typename":"Markup","type":"EM","start":0,"end":214,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:8a641d19e810_14":{"__typename":"Paragraph","id":"8a641d19e810_14","name":"2400","type":"P","href":null,"layout":null,"metadata":null,"text":"Question: What kind of recommender systems does your team focus on?","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"STRONG","start":0,"end":67,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:8a641d19e810_15":{"__typename":"Paragraph","id":"8a641d19e810_15","name":"42b3","type":"P","href":null,"layout":null,"metadata":null,"text":"Xiangting Kong: The main focus of our team is the advertising recommendation system, responsible for the optimization of the advertising training platform. Tencent advertising recommendations system consists of parts including offline feature engineering, training platform, online inference system, online feature engineering and play platform. Advertising recommendations is a process of gradual filtering. Sorting stages include recall, pre-ranking, and ranking. Each stage has different requirements. The rapid investigation and iteration of the model puts forth higher requirements for training performance.","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"STRONG","start":0,"end":16,"href":null,"anchorType":null,"userId":null,"linkMetadata":null},{"__typename":"Markup","type":"EM","start":0,"end":612,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"ImageMetadata:0*cagsGZ-N39ox908d":{"__typename":"ImageMetadata","id":"0*cagsGZ-N39ox908d","originalHeight":643,"originalWidth":999,"focusPercentX":null,"focusPercentY":null,"alt":"The main focus of our team is the advertising recommendation system, responsible for the optimization of the advertising training platform. Tencent advertising recommendations system consists of parts including offline feature engineering, training platform, online inference system, online feature engineering and play platform. Advertising recommendations is a process of gradual filtering. Sorting stages include recall, pre-ranking, and ranking. Each stage has different requirements. The rapid i"},"Paragraph:8a641d19e810_16":{"__typename":"Paragraph","id":"8a641d19e810_16","name":"2a95","type":"IMG","href":null,"layout":"INSET_CENTER","metadata":{"__ref":"ImageMetadata:0*cagsGZ-N39ox908d"},"text":"From Xiangting Kong’s GTC Spring 2021 Presentation","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"EM","start":0,"end":50,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:8a641d19e810_17":{"__typename":"Paragraph","id":"8a641d19e810_17","name":"80c0","type":"P","href":null,"layout":null,"metadata":null,"text":"Question: How does your team conduct training?","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"STRONG","start":0,"end":46,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:8a641d19e810_18":{"__typename":"Paragraph","id":"8a641d19e810_18","name":"c46f","type":"P","href":null,"layout":null,"metadata":null,"text":"Xiangting Kong: We organize some technology sharing every one or two weeks.","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"STRONG","start":0,"end":15,"href":null,"anchorType":null,"userId":null,"linkMetadata":null},{"__typename":"Markup","type":"EM","start":0,"end":75,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:8a641d19e810_19":{"__typename":"Paragraph","id":"8a641d19e810_19","name":"15ed","type":"P","href":null,"layout":null,"metadata":null,"text":"Question: How does your team evaluate your recommender systems? fine tune?","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"STRONG","start":0,"end":74,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:8a641d19e810_20":{"__typename":"Paragraph","id":"8a641d19e810_20","name":"1ae3","type":"P","href":null,"layout":null,"metadata":null,"text":"Xiangting Kong: Through our recommendation system, we optimize the algorithm strategy, add more samples and features, and assess whether it can drive the increase of income. The accuracy of the advertisement recommendation can be improved by training more sample data, by adding more sample features. But this leads to longer training time and effects the update frequency of the model. In order to ensure that the model updates will not be derailed, the training performance of the model needs to be continuously improved. After the training model performance is improved, more data can be trained to improve the accuracy of the model, thereby increasing the advertising revenue.","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"STRONG","start":0,"end":16,"href":null,"anchorType":null,"userId":null,"linkMetadata":null},{"__typename":"Markup","type":"EM","start":0,"end":680,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:8a641d19e810_21":{"__typename":"Paragraph","id":"8a641d19e810_21","name":"1b10","type":"P","href":null,"layout":null,"metadata":null,"text":"Question: How do you optimize your recommender systems? For example, it is our understanding that Tencent uses HugeCTR for embeddings optimization. How has this helped you optimize your workflow?","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"STRONG","start":0,"end":195,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:8a641d19e810_22":{"__typename":"Paragraph","id":"8a641d19e810_22","name":"9344","type":"P","href":null,"layout":null,"metadata":null,"text":"Xiangting Kong: HugeCTR, as a recommendation training framework, is integrated into the advertising recommendation training system to make the update frequency of model training faster, and more samples can be trained to improve online 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CSR type training data is generated so that the data can be directly read on the GPU for training. Through our optimization of the data processing pipeline, the CPU load is greatly reduced and the GPU utilization is greatly improved.","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"STRONG","start":0,"end":15,"href":null,"anchorType":null,"userId":null,"linkMetadata":null},{"__typename":"Markup","type":"EM","start":0,"end":357,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:8a641d19e810_31":{"__typename":"Paragraph","id":"8a641d19e810_31","name":"422f","type":"P","href":null,"layout":null,"metadata":null,"text":"Question: If a team lead was just starting out and currently evaluating building, deploying, and optimizing recommenders for their company….what advice would you relay to help them accelerate or streamline their recommender workflows?","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"STRONG","start":0,"end":234,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:8a641d19e810_32":{"__typename":"Paragraph","id":"8a641d19e810_32","name":"4fae","type":"P","href":null,"layout":null,"metadata":null,"text":"Xiangting Kong: Choose a mature technical framework and be compatible with the community ecosystem to facilitate subsequent system upgrades.","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"STRONG","start":0,"end":15,"href":null,"anchorType":null,"userId":null,"linkMetadata":null},{"__typename":"Markup","type":"EM","start":0,"end":140,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:8a641d19e810_33":{"__typename":"Paragraph","id":"8a641d19e810_33","name":"8781","type":"H3","href":null,"layout":null,"metadata":null,"text":"Additional Community Resources to Consider","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:8a641d19e810_34":{"__typename":"Paragraph","id":"8a641d19e810_34","name":"1f88","type":"P","href":null,"layout":null,"metadata":null,"text":"As NVIDIA is committed to streamlining recommender workflows, we incorporate best practices, insights, and learnings from the broader industry, including innovative early adopters like Tencent. 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