Using hypergraphs to improve product retrieval

Augmenting query-product graphs with hypergraphs describing product-product relationships improves recall score by more than 48%.

Information retrieval engines like the one that helps Amazon customers find products in the Amazon Store commonly rely on bipartite graphs that map queries to products. The graphs are typically based on customer behaviors: if enough customers executing the same query click the link to a product or buy that product, the graph will include an edge between query and product. A graph neural network (GNN) can then ingest the graph and predict edges corresponding to new queries.

This approach has two drawbacks. One is that most products in the Amazon Store belong to the long tail of items that are rarely searched for, which means that they don’t have enough associated data to make GNN training reliable. Conversely, when handling long-tail queries, a GNN will tend to match them to popular but probably unrelated products, simply because they have a high click and purchase rate overall. This phenomenon is known as disassortative mixing.

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In a paper we presented at the ACM Conference on Web Search and Data Mining (WSDM), we address both of these problems by augmenting the bipartite query-product graph with information about which products customers tend to look at during the same online shopping sessions. The idea is that knowing which types of products are related to each other can help the GNN generalize from high-frequency to low-frequency queries.

To capture the information about product relationships, we use a hypergraph, a generalization of the graph structure; where an edge in an ordinary graph links exactly two nodes, an edge in a hypergraph can link multiple nodes. Other information retrieval approaches have used product similarity to improve performance, but modeling product similarity with the hypergraph allows us to use GNNs for prediction, so we can exploit the added structure available in graph representations of data.

In tests, we compared our approach to one that uses GNNs on a bipartite graph only and found that the addition of the hypergraph improved the mean reciprocal rank of the results, which assigns a higher score the closer the correct answer is to the top of a ranked list, by almost 25% and the recall score, which measures the percentage of correct answers retrieved, by more than 48%.

Two-channel architecture

GNNs produce vector representations, or embeddings, of individual graph nodes that capture information about their neighbors. The process is iterative: the first embedding captures only information about the object associated with the node — in our case, product descriptions or query semantics. The second embedding combines the first embedding with those of the node’s immediate neighbors; the third embedding extends the node’s neighborhood by one hop; and so on. Most applications use one- or two-hop embeddings.

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The embedding of a hypergraph modifies this procedure slightly. The first iteration, as in the standard case, embeds each of the item nodes individually. The second iteration creates an embedding for the entirety of each hyperedge. The third iteration then produces an embedding for each node that factors in both its own content-level embedding and the embeddings of all the hyperedges it touches.

The architecture of our model has two channels, one for the query-item bipartite graph and one for the item-item hypergraph. Each passes to its own GNN (a graph convolution network), yielding an embedding for each node.

Hypergraph search.jpeg
An overview of the hypergraph-augmented product retrieval method.

During training, an attention mechanism learns how much weight to give the embedding produced by each channel. A common query with a few popular associated products, for instance, may be well represented by the standard GNN embedding of the bipartite graph. A rarely purchased item, by contrast, associated with a few diverse queries, may benefit from greater weighting of the hypergraph embedding.

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To maximize the quality of our model’s predictions, we also experimented with two different unsupervised pretraining methods. One is a contrastive-learning approach in which the GNN is fed pairs of training examples. Some are positive pairs, whose embeddings should be as similar as possible, and some are negative pairs, whose embeddings should be as different as possible.

Following existing practice, we produce positive pairs by randomly deleting edges or nodes of a source graph, so the resulting graphs are similar but not identical. Negative pairs pair the source graph with a different, random graph. We extend this procedure to the hypergraph and ensure consistency between the two channels’ training data; e.g., a node deleted from one channel’s inputs will also be deleted from the other channel’s.

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We also experiment with DropEdge, a procedure in which, in successive training epochs, slightly different versions of the same graph are used, with a few edges randomly dropped. This prevents overfitting and oversmoothing, as it encourages the GNN to learn more abstract representations of its inputs.

Pretraining dramatically improves the quality of both our two-channel model and the baseline GNN. But it also increases the discrepancy between the two. That is, our approach by itself sometimes yields only a modest improvement over the baseline model. But our approach with pretraining outperforms the baseline model with pretraining by a larger margin.

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Shape the Future of Cloud Computing Are you a graduate student passionate about Automated Reasoning and its real-world applications? Join our team of innovators and embark on a journey to revolutionize cloud computing through cutting-edge automated reasoning techniques.Our tools are called billions of times daily, powering the backbone of Amazon's products and services. We are changing the way computer systems are developed and operated, raising the bar for security, durability, availability, and quality. As an Applied Science Intern, you'll have the opportunity to work alongside our brilliant scientists and contribute to groundbreaking projects. From distributed proof search and SAT/SMT solvers to program analysis, synthesis, and verification, you'll tackle complex challenges at the intersection of theory and practice, driving innovation and delivering tangible value to our customers. This internship is not just about executing tasks – you'll explore novel approaches to solving intricate automated reasoning problems. You'll dive deep into cutting-edge research, leveraging your expertise to develop innovative solutions. You'll work on deploying your solutions into production, witnessing the real-world impact of your contributions. Throughout your journey, you'll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge research papers, and mentorship from industry luminaries. This immersive experience will not only sharpen your technical skills but also cultivate your ability to think critically, communicate effectively, and thrive in a fast-paced, innovative environment. Join us and be part of a team that is shaping the future of cloud computing through the power of Automated Reasoning. Apply now and unlock your potential! Amazon has positions available for Automated Reasoning Applied Science Internships in, but not limited to, Arlington, VA; Boston, MA; Cupertino, CA; Minneapolis, MN; New York, NY; Portland, OR; Santa Clara, CA; Seattle, WA; Bellevue, WA; Santa Clara, CA; Sunnyvale, CA. Key job responsibilities We are particularly interested in candidates with expertise in: Theorem Proving, Boolean Satisfiability Solvers, Bounded Model Checking, Deductive Verification, Programming/Scripting Languages, Abstract Interpretation, Automated Reasoning, Static/Program Analysis, Program Synthesis In this role, you will work alongside global experts to develop and implement novel, scalable algorithms and modeling techniques that advance the state-of-the-art in areas at the intersection of Natural Language Processing and Speech Technologies. You will tackle challenging, groundbreaking research problems on production-scale data, with a focus on natural language processing, speech recognition, text-to-speech (TTS), text recognition, question answering, NLP models (e.g., LSTM, transformer-based models), signal processing, information extraction, conversational modeling, audio processing, speaker detection, large language models, multilingual modeling, and more. The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment. Key job responsibilities We are particularly interested in candidates with expertise in: Theorem Proving, Boolean Satisfiability Solvers, Bounded Model Checking, Deductive Verification, Programming/Scripting Languages, Abstract Interpretation, Automated Reasoning, Static/Program Analysis, Program Synthesis In this role, you will work alongside global experts to develop and implement novel, scalable algorithms and modeling techniques that advance the state-of-the-art in areas at the intersection of Natural Language Processing and Speech Technologies. You will tackle challenging, groundbreaking research problems on production-scale data, with a focus on natural language processing, speech recognition, text-to-speech (TTS), text recognition, question answering, NLP models (e.g., LSTM, transformer-based models), signal processing, information extraction, conversational modeling, audio processing, speaker detection, large language models, multilingual modeling, and more. The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment.
US, WA, Seattle
Unleash Your Potential as an AI Trailblazer At Amazon, we're on a mission to revolutionize the way people discover and access information. Our Applied Science team is at the forefront of this endeavor, pushing the boundaries of recommender systems and information retrieval. We're seeking brilliant minds to join us as interns and contribute to the development of cutting-edge AI solutions that will shape the future of personalized experiences. As an Applied Science Intern focused on Recommender Systems and Information Retrieval in Machine Learning, you'll have the opportunity to work alongside renowned scientists and engineers, tackling complex challenges in areas such as deep learning, natural language processing, and large-scale distributed systems. Your contributions will directly impact the products and services used by millions of Amazon customers worldwide. Imagine a role where you immerse yourself in groundbreaking research, exploring novel machine learning models for product recommendations, personalized search, and information retrieval tasks. You'll leverage natural language processing and information retrieval techniques to unlock insights from vast repositories of unstructured data, fueling the next generation of AI applications. Throughout your journey, you'll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge research papers, and mentorship from industry luminaries. This immersive experience will not only sharpen your technical skills but also cultivate your ability to think critically, communicate effectively, and thrive in a fast-paced, innovative environment where bold ideas are celebrated. Join us at the forefront of applied science, where your contributions will shape the future of AI and propel humanity forward. Seize this extraordinary opportunity to learn, grow, and leave an indelible mark on the world of technology. Amazon has positions available for Machine Learning Applied Science Internships in, but not limited to Arlington, VA; Bellevue, WA; Boston, MA; New York, NY; Palo Alto, CA; San Diego, CA; Santa Clara, CA; Seattle, WA. Key job responsibilities We are particularly interested in candidates with expertise in: Knowledge Graphs and Extraction, Programming/Scripting Languages, Time Series, Machine Learning, Natural Language Processing, Deep Learning,Neural Networks/GNNs, Large Language Models, Data Structures and Algorithms, Graph Modeling, Collaborative Filtering, Learning to Rank, Recommender Systems In this role, you'll collaborate with brilliant minds to develop innovative frameworks and tools that streamline the lifecycle of machine learning assets, from data to deployed models in areas at the intersection of Knowledge Management within Machine Learning. You will conduct groundbreaking research into emerging best practices and innovations in the field of ML operations, knowledge engineering, and information management, proposing novel approaches that could further enhance Amazon's machine learning capabilities. The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment. A day in the life - Design, implement, and experimentally evaluate new recommendation and search algorithms using large-scale datasets - Develop scalable data processing pipelines to ingest, clean, and featurize diverse data sources for model training - Conduct research into the latest advancements in recommender systems, information retrieval, and related machine learning domains - Collaborate with cross-functional teams to integrate your innovative solutions into production systems, impacting millions of Amazon customers worldwide - Communicate your findings through captivating presentations, technical documentation, and potential publications, sharing your knowledge with the global AI community