In this paper, we present NxtPost, a deployed user-to-post content based sequential recommender system for Facebook Groups. Inspired by recent advances in NLP, we have adapted a Transformer based model to the domain of sequential recommendation. We explore causal masked multi-head attention that optimizes both short and long-term user interests. From a user's past activities validated by defined safety process, NxtPost seeks to learn a representation for the user's dynamic content preference and to predict the next post user may be interested in. In contrast to previous Transformer based methods, we do not assume that the recommendable posts have a fixed corpus. Accordingly, we use an external item/token embedding to extend a sequence-based approach to a large vocabulary. We achieve 49% abs. improvement in offline evaluation. As a result of NxtPost deployment, 0.6% more users are meeting new people, engaging with the community, sharing knowledge and getting support. The paper shares our experience in developing a personalized sequential recommender system, lessons deploying the model for cold start users, how to deal with freshness, and tuning strategies to reach higher efficiency in online A/B experiments.

In this paper, we present Que2Search, a deployed query and product understanding system for search. Que2Search leverages multi-task and multi-modal learning approaches to train query and product representations. We achieve over 5% absolute offline relevance improvement and over 4% online engagement gain over state-of-the-art Facebook product understanding system by combining the latest multilingual natural language understanding architectures like XLM and XLM-R with multi-modal fusion techniques. In this paper, we describe how we deploy XLM-based search query understanding model that runs <1.5ms @P99 on CPU at Facebook scale, which has been a significant challenge in the industry. We also describe what model optimizations worked (and what did not) based on numerous offline and online A/B experiments. We deploy Que2Search to Facebook Marketplace Search and share our deployment experience to production and tuning tricks to achieve higher efficiency in online A/B experiments. Que2Search has demonstrated gains in production applications and operates at Facebook scale.

In this paper, we present VisRel, a deployed large-scale media search system that leverages text understanding, media understanding, and multimodal technologies to deliver a modern multimedia search experience. We share our insight on developing image and video understanding models for content retrieval, training efficient and effective media-to-query relevance models, and refining online and offline metrics to measure the success of one of the largest media search databases in the industry. We summarize our learnings gathered from hundreds of A/B test experiments and describe the most effective technical approaches. The techniques presented in this work have contributed 34% (abs.) improvement to media-to-query relevance and 10% improvement to user engagement. We believe that this work can provide practical solutions and insights for engineers who are interested in applying media understanding technologies to empower multimedia search systems that operate at Facebook scale.

In this paper, we present GrokNet, a deployed image recognition system for commerce applications. GrokNet leverages a multi-task learning approach to train a single computer vision trunk. We achieve a 2.1x improvement in exact product match accuracy when compared to the previous state-of-the-art Facebook product recognition system. We achieve this by training on 7 datasets across several commerce verticals, using 80 categorical loss functions and 3 embedding losses. We share our experience of combining diverse sources with wide-ranging label semantics and image statistics, including learning from human annotations, user-generated tags, and noisy search engine interaction data. GrokNet has demonstrated gains in production applications and operates at Facebook scale.

In this paper we present a deployed image recognition system used in a large scale commerce search engine, which we call MSURU. It is designed to process product images uploaded daily to Facebook Marketplace. Social commerce is a growing area within Facebook and understanding visual representations of product content is important for search and recommendation applications on Marketplace. In this paper, we present techniques we used to develop efficient large-scale image classifiers using weakly supervised search log data. We perform extensive evaluation of presented techniques, explain practical experience of developing large-scale classification systems and discuss challenges we faced. Our system, MSURU out-performed current state of the art system developed at Facebook [23] by 16% in e-commerce domain. MSURU is deployed to production with significant improvements in search success rate and active interactions on Facebook Marketplace.