xDeepFM: A novel approach for combining explicit and implicit feature interactions in recommender systems. Recommender systems are crucial for many web applications, and their success often relies on the ability to identify and utilize combinatorial features from raw data. Traditional methods for crafting these features can be time-consuming and costly, especially in large-scale systems. Factorization-based models have emerged as a solution, as they can automatically learn patterns of combinatorial features and generalize to unseen features. Recently, deep neural networks (DNNs) have been proposed to learn both low- and high-order feature interactions, but they generate feature interactions implicitly and at the bit-wise level. xDeepFM, or eXtreme Deep Factorization Machine, is a novel model that addresses this issue by combining a Compressed Interaction Network (CIN) with a classical DNN. The CIN generates feature interactions explicitly and at the vector-wise level, sharing some functionalities with convolutional neural networks (CNNs) and recurrent neural networks (RNNs). This combination allows xDeepFM to learn certain bounded-degree feature interactions explicitly while also learning arbitrary low- and high-order feature interactions implicitly. Recent research has shown that xDeepFM outperforms state-of-the-art models in various experiments conducted on real-world datasets. Practical applications of xDeepFM include personalized advertising, feed ranking, and click-through rate (CTR) prediction. One company case study demonstrates the effectiveness of xDeepFM in improving CTR prediction accuracy and reducing overfitting in web applications. In conclusion, xDeepFM offers a promising approach to combining explicit and implicit feature interactions in recommender systems, providing a more efficient and accurate solution for various applications. As machine learning continues to evolve, models like xDeepFM will play a crucial role in advancing the field and improving the performance of web-scale systems.
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Dive into ML glossary with 650+ Machine Learning & AI terms. Understand concepts from ‘area under curve’ to ‘large language models’. More than a list - our ML Glossary is your key to the industry applications & latest papers in AI.
Cross-lingual Language Models (XLMs) enable natural language processing tasks to be performed across multiple languages, improving performance and generalization in multilingual contexts. Cross-lingual Language Models (XLMs) have emerged as a powerful tool for natural language processing (NLP) tasks, enabling models to work effectively across multiple languages. By leveraging pre-trained models like XLM-RoBERTa, researchers have been able to achieve competitive performance in various tasks, such as acronym extraction, named entity recognition, and sentiment analysis. Recent research has focused on improving the performance of XLMs in different NLP tasks. For example, the Domain Adaptive Pretraining study adapted XLM-RoBERTa embeddings for multilingual acronym extraction, while the LLM-RM at SemEval-2023 Task 2 paper used XLM-RoBERTa for multilingual complex named entity recognition. These studies demonstrate the potential of XLMs in handling diverse languages and tasks. However, there are challenges in using XLMs, such as the high computational cost of processing long documents and the need for fine-tuning on specific tasks. To address these issues, researchers have proposed unsupervised methods like Language-Agnostic Weighted Document Representations (LAWDR), which derive document representations without fine-tuning, making them more practical in resource-limited settings. Practical applications of XLMs include: 1. Multilingual chatbots: XLMs can be used to develop chatbots that understand and respond to user queries in multiple languages, improving user experience and accessibility. 2. Cross-lingual sentiment analysis: Companies can use XLMs to analyze customer feedback in different languages, helping them make data-driven decisions and improve their products and services. 3. Machine translation: XLMs can be employed to improve the quality of machine translation systems, enabling more accurate translations between languages. A company case study is Unbabel, which leverages XLMs to provide AI-powered translation services. By using XLMs, Unbabel can offer high-quality translations across multiple languages, helping businesses communicate effectively with their global audience. In conclusion, XLMs have the potential to revolutionize NLP tasks by enabling models to work effectively across multiple languages. As research continues to advance, we can expect even more powerful and efficient cross-lingual models, opening up new possibilities for multilingual applications and services.
XLM-R: A powerful multilingual language model for cross-lingual understanding and transfer learning. Multilingual language models have revolutionized natural language processing (NLP) by enabling cross-lingual understanding and transfer learning across multiple languages. XLM-R is a state-of-the-art Transformer-based masked language model that has been pretrained on a massive dataset of over 100 languages, making it highly effective for a wide range of cross-lingual tasks. Recent research has focused on improving XLM-R's performance and scalability. For instance, larger-scale versions of XLM-R, such as XLM-R XL and XLM-R XXL, have demonstrated significant improvements in accuracy on benchmarks like XNLI. These models have also shown strong performance on high-resource languages while greatly enhancing low-resource languages. Another area of interest is the combination of static and contextual multilingual embeddings. By extracting static embeddings from XLM-R and aligning them using techniques like VecMap, researchers have achieved high-quality, highly multilingual static embeddings. Continued pre-training of XLM-R with these aligned embeddings has led to positive results for complex semantic tasks. To overcome the vocabulary bottleneck in multilingual masked language models, XLM-V has been introduced. This model assigns vocabulary capacity to achieve sufficient coverage for each individual language, resulting in more semantically meaningful and shorter tokenizations compared to XLM-R. XLM-V has outperformed XLM-R on various tasks, including natural language inference, question answering, and named entity recognition. In summary, XLM-R and its variants have made significant strides in cross-lingual understanding and transfer learning. Practical applications of these models include multilingual sentiment analysis, machine translation, and information extraction. As research continues to advance, we can expect further improvements in the performance and scalability of multilingual language models, making them even more valuable tools for developers working with diverse languages and NLP tasks.