EmTaggeR: A Word Embedding Based Novel Method for Hashtag Recommendation on Twitter
01 Nov 2017-pp 1025-1032
TL;DR: In this article, the authors proposed a new methodology for hashtag recommendation for microblog posts, specifically Twitter, based on a training-testing framework that builds on the top of the concept of word embedding.
Abstract: The hashtag recommendation problem addresses recommending (suggesting) one or more hashtags to explicitly tag a post made on a given social network platform, based upon the content and context of the post. In this work, we propose a novel methodology for hashtag recommendation for microblog posts, specifically Twitter. The methodology, EmTaggeR, is built upon a training-testing framework that builds on the top of the concept of word embedding. The training phase comprises of learning word vectors associated with each hashtag, and deriving a word embedding for each hashtag. We provide two training procedures, one in which each hashtag is trained with a separate word embedding model applicable in the context of that hashtag, and another in which each hashtag obtains its embedding from a global context. The testing phase constitutes computing the average word embedding of the test post, and finding the similarity of this embedding with the known embeddings of the hashtags. The tweets that contain the most-similar hashtag are extracted, and all the hashtags that appear in these tweets are ranked in terms of embedding similarity scores. The top-K hashtags that appear in this ranked list, are recommended for the given test post. Our system produces F1 score of 50.83%, improving over the LDA baseline by around 6.53 times, outperforming the best-performing system known in the literature that provides a lift of 6.42 times. EmTaggeR is a fast, scalable and lightweight system, which makes it practical to deploy in real-life applications.
Citations
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15 Oct 2019
TL;DR: A Graph Convolution Network based Personalized Hashtag Recommendation (GCN-PHR) model, which leverages recently advanced GCN techniques to model the complicate interactions among users, hashtags, and micro-videos and learn their representations.
Abstract: Personalized hashtag recommendation methods aim to suggest users hashtags to annotate, categorize, and describe their posts. The hashtags, that a user provides to a post (e.g., a micro-video), are the ones which in her mind can well describe the post content where she is interested in. It means that we should consider both users' preferences on the post contents and their personal understanding on the hashtags. Most existing methods rely on modeling either the interactions between hashtags and posts or the interactions between users and hashtags for hashtag recommendation. These methods have not well explored the complicated interactions among users, hashtags, and micro-videos. In this paper, towards the personalized micro-video hashtag recommendation, we propose a Graph Convolution Network based Personalized Hashtag Recommendation (GCN-PHR) model, which leverages recently advanced GCN techniques to model the complicate interactions among and learn their representations. In our model, the users, hashtags, and micro-videos are three types of nodes in a graph and they are linked based on their direct associations. In particular, the message-passing strategy is used to learn the representation of a node (e.g., user) by aggregating the message passed from the directly linked other types of nodes (e.g., hashtag and micro-video). Because a user is often only interested in certain parts of a micro-video and a hashtag is typically used to describe the part (of a micro-video) that the user is interested in, we leverage the attention mechanism to filter the message passed from micro-videos to users and hashtags, which can significantly improve the representation capability. Extensive experiments have been conducted on two real-world micro-video datasets and demonstrate that our model outperforms the state-of-the-art approaches by a large margin.
57 citations
01 Jan 2020
TL;DR: This paper reviews core components that enable large-scale querying and indexing for microblogs data, and discusses system-level issues and on-going effort on supporting microblogs through the rising wave of big data systems.
Abstract: Microblogs data is the microlength user-generated data that is posted on the web, e.g., tweets, online reviews, comments on news and social media. It has gained considerable attention in recent years due to its widespread popularity, rich content, and value in several societal applications. Nowadays, microblogs applications span a wide spectrum of interests including targeted advertising, market reports, news delivery, political campaigns, rescue services, and public health. Consequently, major research efforts have been spent to manage, analyze, and visualize microblogs to support different applications. This paper gives a comprehensive review of major research and system work in microblogs data management. The paper reviews core components that enable large-scale querying and indexing for microblogs data. A dedicated part gives particular focus for discussing system-level issues and on-going effort on supporting microblogs through the rising wave of big data systems. In addition, we review the major research topics that exploit these core data management components to provide innovative and effective analysis and visualization for microblogs, such as event detection, recommendations, automatic geotagging, and user queries. Throughout the different parts, we highlight the challenges, innovations, and future opportunities in microblogs data research.
23 citations
TL;DR: This article converts the hashtag recommendation task into a sequence generation problem, and proposes a hybrid neural network approach to extract the features of both texts and images and incorporate them into the sequence-to-sequence model for hashtag recommendation.
Abstract: In the real-world social networks, hashtags are widely applied for understanding the content of an individual microblog. However, users do not always take the initiative in attaching hashtags when posting a microblog so that much effort has been invested for automatically hashtag recommendation. As a new trend, users no longer only post texts but prefer to share with multimodal data, such as images. To deal with these situations, we propose an attention-based multimodal neural network model (AMNN) to learn the representations of multimodal microblogs and recommend relevant hashtags. In this article, we convert the hashtag recommendation task into a sequence generation problem. Then, we propose a hybrid neural network approach to extract the features of both texts and images and incorporate them into the sequence-to-sequence model for hashtag recommendation. Experimental results on the data set collected on Instagram and two public data sets demonstrate that the proposed method outperforms state-of-the-art methods. Our model achieves the best performance in three different metrics: precision, recall, and accuracy. The source code of this article can be obtained from “ https://github.com/w5688414/AMNN .”
20 citations
Posted Content•
TL;DR: Zhang et al. as discussed by the authors proposed a Graph Convolution Network based Personalized Hashtag Recommendation (GCN-PHR) model, which leverages recently advanced GCN techniques to model the complicated interactions among and learn their representations.
Abstract: Personalized hashtag recommendation methods aim to suggest users hashtags to annotate, categorize, and describe their posts. The hashtags, that a user provides to a post (e.g., a micro-video), are the ones which in her mind can well describe the post content where she is interested in. It means that we should consider both users' preferences on the post contents and their personal understanding on the hashtags. Most existing methods rely on modeling either the interactions between hashtags and posts or the interactions between users and hashtags for hashtag recommendation. These methods have not well explored the complicated interactions among users, hashtags, and micro-videos. In this paper, towards the personalized micro-video hashtag recommendation, we propose a Graph Convolution Network based Personalized Hashtag Recommendation (GCN-PHR) model, which leverages recently advanced GCN techniques to model the complicate interactions among and learn their representations. In our model, the users, hashtags, and micro-videos are three types of nodes in a graph and they are linked based on their direct associations. In particular, the message-passing strategy is used to learn the representation of a node (e.g., user) by aggregating the message passed from the directly linked other types of nodes (e.g., hashtag and micro-video). Because a user is often only interested in certain parts of a micro-video and a hashtag is typically used to describe the part (of a micro-video) that the user is interested in, we leverage the attention mechanism to filter the message passed from micro-videos to users and hashtags, which can significantly improve the representation capability. Extensive experiments have been conducted on two real-world micro-video datasets and demonstrate that our model outperforms the state-of-the-art approaches by a large margin.
17 citations
TL;DR: This article proposes a community-based hashtag recommendation framework, which studies hashtag recommendation through tweet similarity task and applies it on communities detected using the Clique percolation method, Louvain algorithm, and label propagation method, and demonstrates that the performance of hashtag recommendation is the best when the communities are generated from the network of users who share similar usage of hashtags.
Abstract: Personalized recommendation automatically predicts the top- $y$ hashtags to a given tweet. Most research in the literature of hashtag recommendation focused on the content of the posts such as words and topics. Although these methods have measured the performance of hashtag recommendation on large data sets, there is a lack of analysis on how these methods perform on small communities. Motivated by the well-studied research area of community detection algorithms that aggregate strongly connected users with similar interests and behaviors, in this article, we propose a community-based hashtag recommendation framework, which studies hashtag recommendation through tweet similarity task and applies it on communities detected using the Clique percolation method, Louvain algorithm, and label propagation method. The detected communities are extracted from four social network constructions based on following, mention, hashtag, and topic. Compared to the three state-of-the-art hashtag recommendation methods, our extensive experiments show that our community-based method outperforms these methods, thus giving a higher hit rate. Our in-depth analysis demonstrates that the performance of hashtag recommendation is the best when the communities are generated using the Clique percolation method (CPM) from the network of users who share similar usage of hashtags.
15 citations
References
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6,832 citations
22 May 2010
TL;DR: This work describes a Natural Language Processing software framework which is based on the idea of document streaming, i.e. processing corpora document after document, in a memory independent fashion, and implements several popular algorithms for topical inference, including Latent Semantic Analysis and Latent Dirichlet Allocation in a way that makes them completely independent of the training corpus size.
Abstract: Large corpora are ubiquitous in today's world and memory
quickly becomes the limiting factor in practical applications
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VSM implementations, which is their scalability and ease of
use. We describe a Natural Language Processing software
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3,965 citations