AMNN: Attention-Based Multimodal Neural Network Model for Hashtag Recommendation

Yang, Qi; Wu, Gaosheng; Li, Yuhua; Li, Ruixuan; Gu, Xiwu; Deng, Huicai; Wu, Junzhuang

doi:10.1109/tcss.2020.2986778

Cited by 26 publications

(17 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These solutions use pattern mining for hashtag recommendation [3,13,22]. Other solutions explore the deep learning architectures to learn the diferent patterns, and behaviors from the collection of tweets, and then use inference to recommend the hashtags of the new tweet [7,24,35,44,46]. From our analysis of the state-of-the-art hashtag recommendation solutions, we can say that the pattern mining solutions require high computational time compared to the deep learning solutions.…”

Section: Motivationsmentioning

confidence: 99%

An Efficient and Accurate GPU-based Deep Learning Model for Multimedia Recommendation

Djenouri

Belhadi

Srivastava

et al. 2023

ACM Trans. Multimedia Comput. Commun. Appl.

View full text Add to dashboard Cite

This paper proposes the use of deep learning in human-computer interaction and presents a new explainable hybrid framework for recommending relevant hashtags on a set of orpheline tweets orpheline tweet: It is a tweet with hashtags. The approach starts by determining the set of batches used in the convolution neural network based on frequent pattern mining solutions. The convolutional neural network is then applied to the set of batches of tweets to learn the hashtags of the tweets. An optimization strategy has been proposed to accurately perform the learning process by reducing the number of frequent patterns. Moreover, eXplainable AI (XAI) is introduced for hashtag recommendations by analyzing the user preferences and understanding the different weights of the deep learning model used in the learning process. This is performed by learning the hyper-parameters of the deep architecture using the genetic algorithm. GPU computing is also investigated to achieve high speed and enable the execution of the overall framework in real time. Extensive experimental analysis has been performed to show that our methodology is useful on different collections of tweets. The experimental results clearly show the efficiency of our proposed approach compared to baseline approaches in terms of both runtime and accuracy. Thus, the proposed solution achieves an accuracy of \(90\% \) when analyzing complex Wikipedia data while the other algorithms did not achieve \(85\% \) when processing the same amount of data.

show abstract

Section: Motivationsmentioning

confidence: 99%

An Efficient and Accurate GPU-based Deep Learning Model for Multimedia Recommendation

Djenouri

Belhadi

Srivastava

et al. 2023

ACM Trans. Multimedia Comput. Commun. Appl.

View full text Add to dashboard Cite

show abstract

“…A recent line of work on tag recommendation takes a generative approach (Wang et al, 2019;Yang et al, 2020b). However, their application of GRU shows that tags are treated as an ordered sequence, neglecting the orderless yet interrelated characteristics of tags.…”

Section: Related Workmentioning

confidence: 99%

“…AR (1-to-1 vs. 1-to-M): A generalized version of the autoregressive (AR) tag generation models (Wang et al, 2019;Yang et al, 2020b). We employ the Transformer (Vaswani et al, 2017) encoderdecoder architecture, but replace the encoder with BERT for fair comparison with our SOG.…”

Section: Baselinesmentioning

confidence: 99%

“…On the opposite side of the spectrum are the recent studies employing an autoregressive (AR) generation model for tag recommendation (Wang et al, 2019;Yang et al, 2020b) where a GRU decoder enables the modeling of the dependency among the generated tags. While this approach considers dependency present in a sequence of tags, it overlooks the nature of tag recommendation where the output is a set of tags (orderlessness) rather than a sequence.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Leveraging Order-Free Tag Relations for Context-Aware Recommendation

Kang¹,

Kim²,

Shin³

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

Tag recommendation relies on either a ranking function for top-k tags or an autoregressive generation method. However, the previous methods neglect one of two seemingly conflicting yet desirable characteristics of a tag set: orderlessness and inter-dependency. While the ranking approach fails to address the interdependency among tags when they are ranked, the autoregressive approach fails to take orderlessness into account because it is designed to utilize sequential relations among tokens. We propose a sequence-oblivious generation method for tag recommendation, in which the next tag to be generated is independent of the order of the generated tags and the order of the ground truth tags occurring in training data. Empirical results on two different domains, Instagram and Stack Overflow, show that our method is significantly superior to the previous approaches.

show abstract

“…In recent years, various methods recommend relevant hashtags to a given microblog by using statistical approaches [1], [2] and neural network approaches [3]- [9] based on textual content. Nevertheless, learning only content in microblogs lacks personalization since the same content from different users might have different meanings depending on (a) Example of multiple relations (user-hashtag interaction, user-user social, and hashtag-hashtag co-occurrence) and characteristics that reflect user preferences and hashtag attributes.…”

Section: Introductionmentioning

confidence: 99%

PAC-MAN: Multi-Relation Network in Social Community for Personalized Hashtag Recommendation

Padungkiatwattana

Maneeroj

2022

IEEE Access

View full text Add to dashboard Cite

Previous personalized hashtag recommendations have been able to recommend suitable hashtags for a given microblog. Despite their performance improvement, we argue that three challenges remain unexplored. First, prior studies capture user interests solely from user-hashtag interactions that are directly connected (i.e., first-order relations), making them unable to deal with multiple user behaviors, including user-user social and hashtag-hashtag co-occurrence, and also restrict relations from similar users that are indirectly connected (i.e., high-order relations). Second, previous works personalize content at the microblog level, ignoring the personalized aspects that users have for each word in the microblog. Third, past studies capture correlations among hashtags in the same microblog from only the left-side correlations, restricting the right-side correlations. In this paper, we propose a novel integral model for personalized hashtag recommendation named PAC-MAN, which explores high-order multiple relations to model fruitful user and hashtag representation before fusing with word representation for word-level personalization and integrating with sequenceless hashtag correlation for the recommendation. First, to derive fruitful user and hashtag representation from higher-order multiple relations, Multi-relational Attentive Network (MAN) applies GNN to jointly capture relations on three communities: (1) user-hashtag interaction; (2) user-user social; and (3) hashtag-hashtag co-occurrence. Second, to personalize content at a word level, Person-And-Content based BERT (PAC) extends BERT to input not only word representations from the microblog but also the fruitful user representation from MAN, allowing each word to be fused with user aspects. Finally, to capture sequenceless hashtag correlations, the fruitful hashtag representations from MAN that contain the hashtag's community perspectives are inserted into BERT to integrate with the hashtag's wordsemantic perspectives, and a hashtag prediction task is then conducted under the mask concept, enabling hashtag correlations to be obtained from both left and right sides without sequence constraints. Extensive experiments on the Twitter dataset demonstrate that PAC-MAN consistently outperforms state-of-the-art methods, including neural network based and traditional graph based methods, over precision, recall, and F1-score metrics.

show abstract

AMNN: Attention-Based Multimodal Neural Network Model for Hashtag Recommendation

Cited by 26 publications

References 43 publications

An Efficient and Accurate GPU-based Deep Learning Model for Multimedia Recommendation

An Efficient and Accurate GPU-based Deep Learning Model for Multimedia Recommendation

Leveraging Order-Free Tag Relations for Context-Aware Recommendation

PAC-MAN: Multi-Relation Network in Social Community for Personalized Hashtag Recommendation

Contact Info

Product

Resources

About