Post-based collaborative filtering for personalized tag recommendation

Lu, Caimei; Hu, Xiaohua; Park, Jung‐ran; Huang, Jia

doi:10.1145/1940761.1940838

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…Cai et al proposed the LOTD method [31], using point-bypoint regression methods to learn from observed tagging data, and it enhances the interaction between users, items, and tags using low-order polynomials. Lu et al developed a post-based collaborative filtering method [32] based on a ternary social tag network. To capture higher-order collaborative signals in entity interactions, Yu et al applied graph networks to PITF to aggregate neighbor information from multiple layers to generate the final representation of entity pairs [33].…”

Section: Related Work a Tag Recommendationmentioning

confidence: 99%

Pairwise Metric Learning With Angular Margin for Tag Recommendation

et al. 2023

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The users on the Internet have been growing exponentially, and tag recommendation can automatically provide users with a selection of tags of interest to meet their personalized needs. Users can utilize these tags to freely annotate their favorite resources, making them efficient and fast in retrieving related resources. Tensor factorization methods are commonly used in tag recommendation at present. These methods model the user × item × tag interactions, transform the latent feature representations of users, items and tags into low-rank matrices and use inner products for prediction. However, the problem of using inner product is that it does not satisfy the triangle inequality, it ignores the distance relationship among entity pairs and cannot capture the fine-grained preference information. Metric learning in recommendation domains focus on using pairwise loss, which assumes that different categories (such as users, items and tags) have fixed margins. Different categories often have different intra-class variations. With fixed margins, it is often difficult to accurately distinguish between positive and negative samples, thus reducing recommendation performance and limiting the expression ability of the model. In this study, the metric learning method is used to explore the distance relationship among user × item × tag triplet, and the existing metric learning based methods (namely LRML, CML, SML) are applied to the tag recommendation. A pairwise metric learning method with angular margin is proposed, named PMLT. The pairwise distance relationship between user-tag and item-tag is modeled for the information of different entities. And an extra angular margin regularizer is added to the original pairwise loss to control the size of angular margin for user-tag and item-tag respectively. The strength of the constrained angular margin regularizer is controlled to dynamically adjust the distance changes of entity. This method constrains the fixed margin and also the angular margin of user-tag and item-tag. Compared with the traditional metric learning method, this method can capture additional relationship structure and has good recommendation performance. Finally, we conducted extensive experiments on two datasets, LastFm and Movielens, and the experimental results showed that the proposed method PMLT outperform the state-of-the-art baseline in the evaluation metrics Recall@N and NDCG@N, and obtain better prediction quality. We also analyze the influence of different parameters and internal components on the performance of the proposed method, which improves the interpretability of the proposed method.INDEX TERMS Tag recommendation, tensor factorization, metric learning, angular margin.

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Section: Related Work a Tag Recommendationmentioning

confidence: 99%

Pairwise Metric Learning With Angular Margin for Tag Recommendation

et al. 2023

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“…UT y y (6) UP method is also easy, but it can neither give tags for new users nor give users new tags.…”

Section: User Popular (Up) Modelmentioning

confidence: 99%

“…In the conventional recommendation systems, items are recommended for users. Many methods being successfully applied in the conventional recommender are introduced into tag recommendation, like collaborative filtering ( [6]- [8]), association-rules ( [9], [10]), latent semantic analysis, topic models [11], [12]). So, we can take the ideas in these methods into tag recommendation models for improvement.…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Popular Model for Personalized Tag Recommendation

Wang¹,

Luo²

2016

JCP

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Tagging systems have been playing an important role in many websites during the web2.0 age. Users use a social tagging system to effectively mark web resources with personalized tags, which can help them organize and share their items easily. In this work, we put forward a new model IBHP to recommend personalized tags for users. We evaluate our model on a real-world dataset collected on Delicious 1 . Data tests show that our model can get better performance than currently widely used popularity-based methods, which also use the same available information: ternary relations.

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“…LDA is a generative probabilistic model broadly used in the information retrieval field. Recently, researchers have used topic modeling approaches based on LDA to build recommendation systems in various subjects, such as scientific paper recommendation [2][3][4][5][6][7][8][9], music and video Recommendation [10][11][12][13][14][15][16][17][18][19], location recommendation [20][21][22][23][24][25][26], hashtag recommendation [27][28][29][30][31][32][33][34][35][36][37][38][39], travel and tour recommedation [40][41][42][43], app recommendation [44][45][46][47][48], event recommendation [49]…”

Section: Introductionmentioning

confidence: 99%

Recommendation System based on Semantic Scholar Mining and Topic modeling: A behavioral analysis of researchers from six conferences

Jelodar,

Wang,

Rabbani

et al. 2018

Preprint

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Recommendation systems have an important place to help online users in the internet society. Recommendation Systems in computer science are of very practical use these days in various aspects of the Internet portals, such as social networks, and library websites. There are several approaches to implement recommendation systems, Latent Dirichlet Allocation (LDA) is one the popular techniques in Topic Modeling. Recently, researchers have proposed many approaches based on Recommendation Systems and LDA. According to importance of the subject, in this paper we discover the trends of the topics and find relationship between LDA topics and Scholar-Context-documents. In fact, We apply probabilistic topic modeling based on Gibbs sampling algorithms for a semantic mining from six conference publications in computer science from DBLP dataset. According to our experimental results, our semantic framework can be effective to help organizations to better organize these conferences and cover future research topics.

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Post-based collaborative filtering for personalized tag recommendation

Cited by 8 publications

References 20 publications

Pairwise Metric Learning With Angular Margin for Tag Recommendation

Pairwise Metric Learning With Angular Margin for Tag Recommendation

A New Hybrid Popular Model for Personalized Tag Recommendation

Recommendation System based on Semantic Scholar Mining and Topic modeling: A behavioral analysis of researchers from six conferences

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