Tagging Items Automatically Based on Both Content Information and Browsing Behaviors

Author(s):  
Shen Liu ◽  
Hongyan Liu

Tags have been adopted by many online services as a method to manage their online resources. Effective tagging benefits both users and firms. In real applications providing a user tagging mechanism, only a small portion of tags are usually provided by users. Therefore, an automatic tagging method, which can assign tags to different items automatically, is urgently needed. Previous works on automatic tagging focus on exploring the tagging behavior of users or the content information of items. In online service platforms, users frequently browse items related to their interests, which implies users’ judgment about the underlying features of items and is helpful for automatic tagging. Browsing-behavior records are much more plentiful compared with tagging behavior and easy to collect. However, existing studies about automatic tagging ignore this kind of information. To properly integrate both browsing behaviors and content information for automatic tagging, we propose a novel probabilistic graphical model and develop a new algorithm for the model parameter inference. We conduct thorough experiments on a real-world data set to evaluate and analyze the performance of our proposed method. The experimental results demonstrate that our approach achieves better performance than state-of-the-art automatic tagging methods. Summary of Contribution. In this paper, we study how to automatically assign tags to items in an e-commerce background. Our study is about how to perform item tagging for e-commerce and other online service providers so that consumers can easily find what they need and firms can manage their resources effectively. Specifically, we study if consumer browsing behavior can be utilized to perform the tagging task automatically, which can save efforts of both firms and consumers. Additionally, we transform the problem into how to find the most proper tags for items and propose a novel probabilistic graphical model to model the generation process of tags. Finally, we develop a variational inference algorithm to learn the model parameters, and the model shows superior performance over competing benchmark models. We believe this study contributes to machine learning techniques.

2011 ◽  
Vol 34 (10) ◽  
pp. 1897-1906 ◽  
Author(s):  
Kun YUE ◽  
Wei-Yi LIU ◽  
Yun-Lei ZHU ◽  
Wei ZHANG

2015 ◽  
Vol 43 (1) ◽  
pp. 267-281 ◽  
Author(s):  
Nikita Mishra ◽  
Huazhe Zhang ◽  
John D. Lafferty ◽  
Henry Hoffmann

2020 ◽  
Vol 34 (04) ◽  
pp. 3641-3648 ◽  
Author(s):  
Eli Chien ◽  
Antonia Tulino ◽  
Jaime Llorca

The geometric block model is a recently proposed generative model for random graphs that is able to capture the inherent geometric properties of many community detection problems, providing more accurate characterizations of practical community structures compared with the popular stochastic block model. Galhotra et al. recently proposed a motif-counting algorithm for unsupervised community detection in the geometric block model that is proved to be near-optimal. They also characterized the regimes of the model parameters for which the proposed algorithm can achieve exact recovery. In this work, we initiate the study of active learning in the geometric block model. That is, we are interested in the problem of exactly recovering the community structure of random graphs following the geometric block model under arbitrary model parameters, by possibly querying the labels of a limited number of chosen nodes. We propose two active learning algorithms that combine the use of motif-counting with two different label query policies. Our main contribution is to show that sampling the labels of a vanishingly small fraction of nodes (sub-linear in the total number of nodes) is sufficient to achieve exact recovery in the regimes under which the state-of-the-art unsupervised method fails. We validate the superior performance of our algorithms via numerical simulations on both real and synthetic datasets.


Author(s):  
Javier Herrera-Vega ◽  
Felipe Orihuela-Espina ◽  
Pablo H. Ibargüengoytia ◽  
Uriel A. García ◽  
Dan-El Vila Rosado ◽  
...  

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