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Make various updates including publications
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‎.gitignore‎

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.jekyll-metadata
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node_modules
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Gemfile.lock
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_site/
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_site/
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cache/
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extensions/
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gems/
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specifications/

‎Gemfile‎

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source "https://rubygems.org"
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ruby RUBY_VERSION
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# Hello! This is where you manage which Jekyll version is used to run.
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# When you want to use a different version, change it below, save the
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# bundle exec jekyll serve
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#
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gem "jekyll", "~> 4.3.2"
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gem "minima", "~> 2.5"
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# If you have any plugins, put them here!
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gem 'wdm', '>= 0.1.0' if Gem.win_platform?
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group :jekyll_plugins do
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gem 'jekyll-feed'
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gem 'jekyll-feed', "~> 0.12"
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gem 'jekyll-sitemap'
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gem 'jekyll-paginate'
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gem 'jekyll-seo-tag'
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gem "jekyll", "~> 3.6"
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gem "kramdown-parser-gfm"
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end
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end
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gem "webrick", "~> 1.9"

‎README.md‎

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Homepage/blog of Mahesh Sathiamoorthy.
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# Mahesh Sathiamoorthy's Website
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Homepage/blog of Mahesh Sathiamoorthy.
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## Building and Testing the Website
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This is a Jekyll-based website. Here are the steps to build and test it locally:
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### Prerequisites
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- Docker and Docker Compose installed on your system
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- Git (to clone the repository)
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### Building the Website
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1. **Clone the repository** (if you haven't already):
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```bash
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git clone <repository-url>
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cd madiator.github.com
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```
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2. **Build the Jekyll site using Docker**:
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```bash
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docker compose run --rm jekyll bundle exec jekyll build
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```
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This will generate the static site files in the `_site` directory.
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### Testing on Localhost
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1. **Start the Jekyll development server**:
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```bash
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docker compose up
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```
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This will start the Jekyll server with auto-regeneration enabled.
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2. **Access the website**:
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Open your browser and navigate to `http://localhost:4000`
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3. **Stop the server**:
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Press `Ctrl+C` in the terminal to stop the development server.
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### Alternative: Using Local Ruby Environment
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If you prefer to use a local Ruby environment instead of Docker:
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1. **Install dependencies**:
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```bash
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bundle install
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```
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2. **Build the site**:
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```bash
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bundle exec jekyll build
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```
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3. **Serve locally**:
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```bash
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bundle exec jekyll serve
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```
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Then visit `http://localhost:4000` in your browser.
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### Troubleshooting
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#### RubyGems Version Warning
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If you encounter a warning like:
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```
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Your RubyGems version (3.0.3.1) has a bug that prevents `required_ruby_version` from working for Bundler...
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```
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This typically happens with older Ruby versions (like Ruby 2.6.x). Here are your options:
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1. **Recommended: Use Docker** (avoids all Ruby version issues):
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```bash
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docker compose run --rm jekyll bundle exec jekyll build
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docker compose up
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```
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2. **Update Ruby** (if you want to use local Ruby):
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- Install a newer Ruby version using a version manager like `rbenv` or `rvm`
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- Ruby 3.0+ is recommended for better compatibility
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3. **Ignore the warning** (if everything still works):
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- The warning is just about future compatibility
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- Your current setup may still work for now
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#### Gem Version Conflicts
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If you get an error like:
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```
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You have already activated i18n 1.12.0, but your Gemfile requires i18n 0.9.5. Prepending `bundle exec` to your command may solve this.
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```
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**Solution**: Always use `bundle exec` with Docker commands:
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```bash
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docker compose run --rm jekyll bundle exec jekyll build
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```
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#### Other Common Issues
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- **Permission errors**: Use `sudo` for gem installations if needed
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- **Bundle install fails**: Try `bundle update` or use Docker instead
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- **Port 4000 already in use**: Change the port with `jekyll serve --port 4001`
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### Notes
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- The Docker approach is **strongly recommended** as it avoids Ruby version conflicts and dependency issues
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- The site uses Jekyll 4.3.2 with various plugins for feed generation and other features
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- All site files are generated in the `_site` directory when building
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- The development server automatically regenerates the site when files are changed

‎_includes/menu-header.html‎

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</li>
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<li class="nav-item">
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<a class="nav-link" href="{{site.baseurl}}/blog">Blog</a>
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</li>
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<li class="nav-item">
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<a class="nav-link" href="{{site.baseurl}}/research">Research</a>
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</li>
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<a class="nav-link" href="https://bespokelabs.ai">Bespoke Labs</a>
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</li>
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<li class="nav-item">
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<a class="nav-link" href="{{site.baseurl}}/projects">Projects</a>
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<a class="nav-link" href="https://madiator.substack.com/">AI Blog</a>
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</li>
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<li class="nav-item">
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<a class="nav-link" href="{{site.baseurl}}/art-photo">Art & Photography</a>
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<a class="nav-link" href="http://smarter.substack.com">Personal Writing</a>
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</li>
2016

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<li class="nav-item">
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<a class="nav-link" href="http://newsletter.smarter.blog">Smarter.Blog</a>
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<a class="nav-link" href="{{site.baseurl}}/art-photo">Photography</a>
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</li>

‎_pages/art-photo.md‎

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</div>
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</div>
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</div>
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<div class="jumbotron jumbotron-fluid jumbotron-home pt-0 pb-0 mt-3 mb-2rem bg-lightblue position-relative">
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<!-- <div class="jumbotron jumbotron-fluid jumbotron-home pt-0 pb-0 mt-3 mb-2rem bg-lightblue position-relative">
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<div class="pl-4 pr-0 h-100 tofront">
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<div class="row justify-content-between">
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<div class="col-md-6 pt-6 pb-6 pr-lg-4 align-self-center">
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<div class="col-md-6 d-none d-md-block pr-0" style="background-size:cover;background-image:url(assets/images/charcoal.jpg);"></div>
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</div>
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</div>
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</div>
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</div> -->
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</div> <!-- /container -->

‎docker-compose.yml‎

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jekyll:
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version: '3'
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services:
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jekyll:
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image: jekyll/jekyll:latest
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command: jekyll serve --force_polling
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platform: linux/amd64
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command: sh -c "bundle install && bundle exec jekyll serve --force_polling --host 0.0.0.0"
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ports:
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- 4000:4000
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volumes:

‎index.html‎

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<div class="pl-4 pr-0 h-100 tofront">
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<div class="row justify-content-between">
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<div class="col-md-6 pt-6 pb-6 pr-lg-4 align-self-center">
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<h1 class="mb-3">Research/Current Work</h1>
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I recently started a startup called Bespoke Labs. We are still in stealth and building our product! <br>
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I was a Tech Lead in Google DeepMind and worked on LLM-based recommender models.
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<h1 class="mb-3">Recent Work</h1>
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I started Bespoke Labs in 2024. We focus on RL environment curation and Data curation.<br>
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<br>
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Previously, I was a Tech Lead in Google DeepMind and worked on LLM-based recommender models. In 2021,
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very few people were thinking about applying the bitter lesson to recommender systems.
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I kickstarted a project to scale recommender models using LLMs, which resulted in
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several research publications (and one best paper award) and many impactful launches across recommender systems such as YouTube.
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<br>
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I have been instrumental in scaling up our recommender models and delivered numerous launches that improve either the quality or the performance of recommender models. <br>
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I work on all aspects of recommender models: infrastructure and productionization, research, and engagements. My work benefits surfaces such as YouTube.
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</div>
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<div class="col-md-6 d-none d-md-block pr-0" style="background-size:cover;background-image:url(assets/images/tpu.png);"></div>
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</div>
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<div class="col-md-12 pt-6 pb-6 pr-lg-4 align-self-center">
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<h1 class="mb-3">Selected Publications</h1>
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<ol>
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<li>Etash Guha, Ryan Marten, Sedrick Keh, Negin Raoof, Georgios Smyrnis, .. <b>Maheswaran Sathiamoorthy</b>, Alex Dimakis, Ludwig Schmidt. "OpenThoughts: Data Recipes for Reasoning Models". <a href="https://arxiv.org/abs/2506.04178">arXiv</a>. 2025</li>
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<li>Yashar Deldjoo, Zhankui He, Julian McAuley, Anton Korikov, Scott Sanner, Arnau Ramisa, René Vidal, <b>Maheswaran Sathiamoorthy</b>, Atoosa Kasrizadeh, Silvia Milano, Francesco Ricci. "Recommendation with generative models". <a href="https://arxiv.org/abs/2409.15173">arXiv</a>. 2024</li>
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<li>Yashar Deldjoo, Zhankui He, Julian McAuley, Anton Korikov, Scott Sanner, Arnau Ramisa, René Vidal, <b>Maheswaran Sathiamoorthy</b>, Atoosa Kasirzadeh, Silvia Milano. "A review of modern recommender systems using generative models (gen-recsys)". Proceedings of the 30th ACM SIGKDD conference on Knowledge Discovery and Data Mining. 2024</li>
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<li>Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan H. Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Q. Tran, Jonah Samost, Maciej Kula, Ed H. Chi, <b>Maheswaran Sathiamoorthy</b>. "Recommender Systems with Generative Retrieval". <a href="https://arxiv.org/abs/2305.05065">arxiv</a>. NeurIPS 2023</li>
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<li>Jiaxi Tang, Yoel Drori, Daryl Chang, <b>Maheswaran Sathiamoorthy</b>, Justin Gilmer, Li Wei, Xinyang Yi, Lichan Hong, Ed H. Chi, "Improving Training Stability for Multitask Ranking Models in Recommender Systems". <a href="https://arxiv.org/abs/2302.09178">arxiv</a>. KDD 2023, <b>Best Paper Award</b> in Data Science Track.</li>
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<li>Wang-Cheng Kang, Jianmo Ni, Nikhil Mehta, <b>Maheswaran Sathiamoorthy</b>, Lichan Hong, Ed Chi, Derek Zhiyuan Cheng. "Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction". <a href="https://arxiv.org/abs/2305.06474">arxiv</a>. Workshop on Personalized Generative AI, CIKM 2023.</li>

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