Text Clustering with Limited User Feedback Under Local Metric Learning

2006 ◽  
pp. 132-144 ◽  
Author(s):  
Ruizhang Huang ◽  
Zhigang Zhang ◽  
Wai Lam
Keyword(s):  
Metric Learning ◽  
Text Clustering ◽  
User Feedback

scholarly journals Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems

2019 ◽  
Author(s):  
Xiao Zhou ◽  
Danyang Liu ◽  
Jianxun Lian ◽  
Xing Xie
Keyword(s):  
Recommender Systems ◽  
Metric Learning ◽  
User Feedback ◽  
User Preferences ◽  
Single Type ◽  
Fine Grained ◽  
Neural Learning ◽  
Wide Range ◽  
Memory Network ◽  
Feedback Data

The success of recommender systems in modern online platforms is inseparable from the accurate capture of users' personal tastes. In everyday life, large amounts of user feedback data are created along with user-item online interactions in a variety of ways, such as browsing, purchasing, and sharing. These multiple types of user feedback provide us with tremendous opportunities to detect individuals' fine-grained preferences. Different from most existing recommender systems that rely on a single type of feedback, we advocate incorporating multiple types of user-item interactions for better recommendations. Based on the observation that the underlying spectrum of user preferences is reflected in various types of interactions with items and can be uncovered by latent relational learning in metric space, we propose a unified neural learning framework, named Multi-Relational Memory Network (MRMN). It can not only model fine-grained user-item relations but also enable us to discriminate between feedback types in terms of the strength and diversity of user preferences. Extensive experiments show that the proposed MRMN model outperforms competitive state-of-the-art algorithms in a wide range of scenarios, including e-commerce, local services, and job recommendations.

Gathering User Feedback from Internal Sources to Supplement Formal Usability Studies

2009 ◽  
Author(s):  
Jeffrey J. Smith ◽  
Daniel P. Kelaher ◽  
David T. Windell
Keyword(s):  
User Feedback ◽  
Internal Sources

Query expansion with the minimum user feedback by transductive learning

2005 ◽  
Author(s):  
Masayuki Okabe ◽  
Kyoji Umemura ◽  
Seiji Yamada
Keyword(s):  
Query Expansion ◽  
User Feedback ◽  
Transductive Learning

K-means text clustering algorithm based on density and nearest neighbor

2010 ◽  
Vol 30 (7) ◽  
pp. 1933-1935 ◽  
Author(s):  
Wen-ming ZHANG ◽  
Jiang WU ◽  
Xiao-jiao YUAN
Keyword(s):  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Text Clustering

Dysarthric Speech Recognition Based on Deep Metric Learning

2020 ◽  
Author(s):  
Yuki Takashima ◽  
Ryoichi Takashima ◽  
Tetsuya Takiguchi ◽  
Yasuo Ariki
Keyword(s):  
Speech Recognition ◽  
Metric Learning ◽  
Deep Metric Learning ◽  
Dysarthric Speech

An Improved B-hill Climbing Optimization Technique for Solving the Text Documents Clustering Problem

2020 ◽  
Vol 16 (4) ◽  
pp. 296-306 ◽  
Author(s):  
Laith Mohammad Abualigah ◽  
Essam Said Hanandeh ◽  
Ahamad Tajudin Khader ◽  
Mohammed Abdallh Otair ◽  
Shishir Kumar Shandilya
Keyword(s):  
Optimization Technique ◽  
Document Clustering ◽  
Text Clustering ◽  
Hill Climbing ◽  
Text Documents ◽  
Clustering Problem ◽  
Text Document ◽  
Text Information ◽  
Amount Of Knowledge ◽  
The Hill

Background: Considering the increasing volume of text document information on Internet pages, dealing with such a tremendous amount of knowledge becomes totally complex due to its large size. Text clustering is a common optimization problem used to manage a large amount of text information into a subset of comparable and coherent clusters. Aims: This paper presents a novel local clustering technique, namely, β-hill climbing, to solve the problem of the text document clustering through modeling the β-hill climbing technique for partitioning the similar documents into the same cluster. Methods: The β parameter is the primary innovation in β-hill climbing technique. It has been introduced in order to perform a balance between local and global search. Local search methods are successfully applied to solve the problem of the text document clustering such as; k-medoid and kmean techniques. Results: Experiments were conducted on eight benchmark standard text datasets with different characteristics taken from the Laboratory of Computational Intelligence (LABIC). The results proved that the proposed β-hill climbing achieved better results in comparison with the original hill climbing technique in solving the text clustering problem. Conclusion: The performance of the text clustering is useful by adding the β operator to the hill climbing.

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