We address the problem of real-time recommendation of streaming Twitter hashtags to an incoming stream of news articles. The technical challenge can be framed as large scale topic classification where the set of topics (i.e., hashtags) is huge and highly dynamic. Our main applications come from digital journalism, e.g., for promoting original content to Twitter communities and for social indexing of news to enable better retrieval, story tracking and summarisation. In contrast to state-of-the-art methods that focus on modelling each individual hashtag as a topic, we propose a learning-to-rank approach for modelling hashtag relevance, and present methods to extract time-aware features from highly dynamic content. We present the data collection and processing pipeline, as well as our methodology for achieving low latency, high precision recommendations. Our empirical results show that our method outperforms the state-of-theart, delivering more than 80% precision. Our techniques are implemented in a real-time system 1 , and are currently under user trial with a big news organisation.
Abstract. We present the Insight4News system that connects news articles to social conversations, as echoed in microblogs such as Twitter. Insight4News tracks feeds from mainstream media, e.g., BBC, Irish Times, and extracts relevant topics that summarize the tweet activity around each article, recommends relevant hashtags, and presents complementary views and statistics on the tweet activity, related news articles, and timeline of the story with regard to Twitter reaction. The user can track their own news article or a topic-focused Twitter stream. While many systems tap on the social knowledge of Twitter to help users stay on top of the information wave, none is available for connecting news to relevant Twitter content on a large scale, in real time, with high precision and recall. Insight4News builds on our award winning Twitter topic detection approach and several machine learning components, to deliver news in a social context.
Entity disambiguation, or mapping a phrase to its canonical representation in a knowledge base, is a fundamental step in many natural language processing applications. Existing techniques based on global ranking models fail to capture the individual peculiarities of the words and hence, struggle to meet the accuracy-time requirements of many real-world applications. In this paper, we propose a new system that learns specialized features and models for disambiguating each ambiguous phrase in the English language. We train and validate the hundreds of thousands of learning models for this purpose using a Wikipedia hyperlink dataset with more than 170 million labelled annotations. The computationally intensive training required for this approach can be distributed over a cluster. In addition, our approach supports fast queries, efficient updates and its accuracy compares favorably with respect to other state-of-the-art disambiguation systems.
Recent years have seen a rise in smartphone applications promoting health and well being. We argue that there is a large and unexplored ground within the field of recommender systems (RS) for applications that promote good personal health. During the COVID-19 pandemic, with gyms being closed, the demand for at-home fitness apps increased as users wished to maintain their physical and mental health. However, maintaining long-term user engagement with fitness applications has proved a difficult task. Personalisation of the app recommendations that change over time can be a key factor for maintaining high user engagement. In this work we propose a reinforcement learning (RL) based framework for recommending sequences of body-weight exercises to home users over a mobile application interface. The framework employs a user simulator, tuned to feedback a weighted sum of realistic workout rewards, and trains a neural network model to maximise the expected reward over generated exercise sequences. We evaluate our framework within the context of a large 15 week live user trial, showing that an RL based approach leads to a significant increase in user engagement compared to a baseline recommendation algorithm.
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