Applying Deep Learning To Airbnb Search

The application to search ranking is one of the biggest machine learning success stories at Airbnb. Much of the initial gains were driven by a gradient boosted decision tree model. The gains, however, plateaued over time. This paper discusses the work done in applying neural networks in an attempt to break out of that plateau. We present our perspective not with the intention of pushing the frontier of new modeling techniques. Instead, ours is a story of the elements we found useful in applying neural networks to a real life product. Deep learning was steep learning for us. To other teams embarking on similar journeys, we hope an account of our struggles and triumphs will provide some useful pointers. Bon voyage!Comment: 8 page

Personalized Ranking in eCommerce Search

We address the problem of personalization in the context of eCommerce search. Specifically, we develop personalization ranking features that use in-session context to augment a generic ranker optimized for conversion and relevance. We use a combination of latent features learned from item co-clicks in historic sessions and content-based features that use item title and price. Personalization in search has been discussed extensively in the existing literature. The novelty of our work is combining and comparing content-based and content-agnostic features and showing that they complement each other to result in a significant improvement of the ranker. Moreover, our technique does not require an explicit re-ranking step, does not rely on learning user profiles from long term search behavior, and does not involve complex modeling of query-item-user features. Our approach captures item co-click propensity using lightweight item embeddings. We experimentally show that our technique significantly outperforms a generic ranker in terms of Mean Reciprocal Rank (MRR). We also provide anecdotal evidence for the semantic similarity captured by the item embeddings on the eBay search engine.Comment: Under Revie

Rethinking Personalized Ranking at Pinterest: An End-to-End Approach

In this work, we present our journey to revolutionize the personalized recommendation engine through end-to-end learning from raw user actions. We encode user's long-term interest in Pinner- Former, a user embedding optimized for long-term future actions via a new dense all-action loss, and capture user's short-term intention by directly learning from the real-time action sequences. We conducted both offline and online experiments to validate the performance of the new model architecture, and also address the challenge of serving such a complex model using mixed CPU/GPU setup in production. The proposed system has been deployed in production at Pinterest and has delivered significant online gains across organic and Ads applications

Development of a travel recommender system

Nowadays, e-commerce is facing the problem of information overload, where users are exposed to a vast amount of content, making it more difficult for users to effectively take quality decisions. The need for delivering the right items at the right moment to each user has resulted in making recommendation systems one of the hot topics in research and technology trends, where a good recommendation system might give a key advantage to an e-commerce over its competitors. Industry-leading companies such as Youtube or Amazon introduced the concept of large-scale recommendation systems, where the number of candidate items to consider for recommendation is enormous, and efficient techniques must be applied. The most common way to deal with large-scale recommendation systems nowadays is to build a retrieval model that retrieves a subset of relevant items for the user, and a ranking model that scores and ranks the set of retrieved items. Research on recommendation systems is continuously evolving, where new approaches produce state-of-the-art results, which especially happens thanks to the rise of deep learning. In this thesis, we describe classical and current approaches to recommendation systems, from content-based methods without assuming latent factors and collaborative-filtering methods like matrix factorization, to hybrid approaches, deep learning-based methods, and state-of-the-art approaches. Precisely, we focus on the concept of context-aware methods and multitask methods, which aim to optimize more than one task at a time. In this master thesis, we focus on developing a recommendation system for Stayforlong as a proof of concept. Firstly, we analyze their data and see that we can opt for a hybrid context-aware model since we have at our disposal user features, item features and context features. Another thing that characterizes the data set that we work with is the abundance of implicit feedback and the scarcity of explicit feedback. This drives us to experiment with different model architectures and approaches, focusing on developing a hybrid model that performs a retrieval task and another hybrid model that performs a ranking task. In addition, we check in our experiments the benefits of adding context to our models, the benefits of jointly training a model that optimizes multiple tasks, and the benefits of training a model on an abundant data set, like implicit feedback, and applying transfer learning to fine-tune on explicit feedback the learned representations. Results show that, in rich data scenarios, a context-aware multitask hybrid model trained on implicit feedback and fine-tuned with explicit feedback outperforms other approaches such as training separate retrieval and ranking models, disregarding implicit feedback or not including context features in the models. Finally, we propose as future work a data pipeline for the recommendation system to be used in production, taking into account data freshness and model re-training