Learning dynamic user preference has become an increasingly important component for many online platforms (e.g., video-sharing sites, e-commerce systems) to make sequential recommendations. Previous works have made many efforts to model item-item transitions over user interaction sequences, based on various architectures, e.g., recurrent neural networks and self-attention mechanism. Recently emerged graph neural networks also serve as useful backbone models to capture item dependencies in sequential recommendation scenarios. Despite their effectiveness, existing methods have far focused on item sequence representation with singular type of interactions, and thus are limited to capture dynamic heterogeneous relational structures between users and items (e.g., page view, add-to-favorite, purchase). To tackle this challenge, we design a Multi-Behavior Hypergraph-enhanced Transformer framework (MBHT) to capture both short-term and long-term cross-type behavior dependencies. Specifically, a multi-scale Transformer is equipped with low-rank self-attention to jointly encode behavior-aware sequential patterns from fine-grained and coarse-grained levels. Additionally, we incorporate the global multi-behavior dependency into the hypergraph neural architecture to capture the hierarchical long-range item correlations in a customized manner. Experimental results demonstrate the superiority of our MBHT over various state-of-the-art recommendation solutions across different settings. Further ablation studies validate the effectiveness of our model design and benefits of the new MBHT framework. Our implementation code is released at: https://github.com/yuh-yang/MBHT-KDD22.

This article was published as a part of the Data Science Blogathon. However, we can improve the system's accuracy by leveraging contextual information. Any type of contextual information, like device context, conversational context, and metadata, such as the time the request was issued, etc., could be utilized to customize the underlying ASR model of the conversational agent (Alexa in our case). In this article, we will understand the need for contextual ASR and will also go over how the context vector is created in Alexa and the issues this process presents. Contextual information like the text of a user's request, the history of the user's recent interactions with a virtual assistant, and metadata like the time the request was issued can be used to customize the underlying ASR model to make a context-aware VA.

The concept of machine learning is essentially the same as what it sounds like; it refers to the concept that various forms of technology, such as computers and tablets, can learn something based on programming and other data. Although it has the appearance of an idea from the far future, most people now make regular use of this level of technology. One particularly useful application of this is speech recognition. The technology is utilized by virtual assistants such as Siri and Alexa to do tasks such as reciting reminders, answering queries, and carrying out requests. As machine learning becomes increasingly popular, an increasing number of individuals are deciding to specialize in the field as machine learning engineers.

Machine learning (ML) is the science of helping computers discover patterns and relationships in data instead of being manually programmed. It's a powerful tool for creating personalized and dynamic experiences, and it's already driving everything from Netflix recommendations to autonomous cars. But as more and more experiences are built with ML, it's clear that UXers still have a lot to learn about how to make users feel in control of the technology, and not the other way round. As was the case with the mobile revolution, and the web before that, ML will cause us to rethink, restructure, displace, and consider new possibilities for virtually every experience we build. In the Google UX community, we've started an effort called "human-centered machine learning" (HCML) to help focus and guide that conversation.

There are many contexts in which dyadic data are present. Social networks are a well-known example. In these contexts, pairs of elements are linked building a network that reflects interactions. Explaining why these relationships are established is essential to obtain transparency, an increasingly important notion. These explanations are often presented using text, thanks to the spread of the natural language understanding tasks. Our aim is to represent and explain pairs established by any agent (e.g., a recommender system or a paid promotion mechanism), so that text-based personalisation is taken into account. We have focused on the TripAdvisor platform, considering the applicability to other dyadic data contexts. The items are a subset of users and restaurants and the interactions the reviews posted by these users. We propose the PTER (Personalised TExt-based Reviews) model. We predict, from the available reviews for a given restaurant, those that fit to the specific user interactions. PTER leverages the BERT (Bidirectional Encoders Representations from Transformers) transformer-encoder model. We customised a deep neural network following the feature-based approach, presenting a LTR (Learning To Rank) downstream task. We carried out several comparisons of our proposal with a random baseline and other models of the state of the art, following the EXTRA (EXplanaTion RAnking) benchmark. Our method outperforms other collaborative filtering proposals.

Both in academic and industry-based research, online evaluation methods are seen as the golden standard for interactive applications like recommendation systems. Naturally, the reason for this is that we can directly measure utility metrics that rely on interventions, being the recommendations that are being shown to users. Nevertheless, online evaluation methods are costly for a number of reasons, and a clear need remains for reliable offline evaluation procedures. In industry, offline metrics are often used as a first-line evaluation to generate promising candidate models to evaluate online. In academic work, limited access to online systems makes offline metrics the de facto approach to validating novel methods. Two classes of offline metrics exist: proxy-based methods, and counterfactual methods. The first class is often poorly correlated with the online metrics we care about, and the latter class only provides theoretical guarantees under assumptions that cannot be fulfilled in real-world environments. Here, we make the case that simulation-based comparisons provide ways forward beyond offline metrics, and argue that they are a preferable means of evaluation.

As a 42-year-old, newly single mom, I was a little insecure when I joined Match.com to meet a nice guy. I described myself as a feminist law professor, interested in liberal intellectuals within five years, plus or minus, of my age. The people who contacted me only eroded my confidence, however. I got cryptic messages from much older and more conservative high school grads, pictured on their motorcycles. These suitors and I ostensibly had nothing in common.

Recently, self-learning methods based on user satisfaction metrics and contextual bandits have shown promising results to enable consistent improvements in conversational AI systems. However, directly targeting such metrics by off-policy bandit learning objectives often increases the risk of making abrupt policy changes that break the current user experience. In this study, we introduce a scalable framework for supporting fine-grained exploration targets for individual domains via user-defined constraints. For example, we may want to ensure fewer policy deviations in business-critical domains such as shopping, while allocating more exploration budget to domains such as music. Furthermore, we present a novel meta-gradient learning approach that is scalable and practical to address this problem. The proposed method adjusts constraint violation penalty terms adaptively through a meta objective that encourages balanced constraint satisfaction across domains. We conduct extensive experiments using data from a real-world conversational AI on a set of realistic constraint benchmarks. Based on the experimental results, we demonstrate that the proposed approach is capable of achieving the best balance between the policy value and constraint satisfaction rate.

In this paper, we research the new topic of object effects recommendation in micro-video platforms, which is a challenging but important task for many practical applications such as advertisement insertion. To avoid the problem of introducing background bias caused by directly learning video content from image frames, we propose to utilize the meaningful body language hidden in 3D human pose for recommendation. To this end, in this work, a novel human pose driven object effects recommendation network termed PoseRec is introduced. PoseRec leverages the advantages of 3D human pose detection and learns information from multi-frame 3D human pose for video-item registration, resulting in high quality object effects recommendation performance. Moreover, to solve the inherent ambiguity and sparsity issues that exist in object effects recommendation, we further propose a novel item-aware implicit prototype learning module and a novel pose-aware transductive hard-negative mining module to better learn pose-item relationships. What's more, to benchmark methods for the new research topic, we build a new dataset for object effects recommendation named Pose-OBE. Extensive experiments on Pose-OBE demonstrate that our method can achieve superior performance than strong baselines.

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.