Enhanced Learning to Rank using Cluster-loss Adjustment

Bajaj, Ahsaas; Tiwari, Hemant; Vala, Vanraj

doi:10.1145/3297001.3297010

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2019

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Cited by 2 publications

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“…Very recent work has been done to build a unified mobile search framework using neural approaches to learn high-dimensional representations for mobile apps [16]. Also, there are recent attempts made to use Learning-to-Rank algorithms for optimizing retrieval of mobile notifications triggered by installed apps [17]. However, application embeddings have been rarely utilized to perform query expansion, nearest neighbor analysis and other tasks related to mobile applications.…”

Section: Related Workmentioning

confidence: 99%

RelEmb: A relevance-based application embedding for Mobile App retrieval and categorization

Bajaj¹,

Krishna²,

Rungta³

et al. 2019

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Information Retrieval Systems have revolutionized the organization and extraction of Information. In recent years, mobile applications (apps) have become primary tools of collecting and disseminating information. However, limited research is available on how to retrieve and organize mobile apps on users' devices. In this paper, authors propose a novel method to estimate app-embeddings which are then applied to tasks like app clustering, classification, and retrieval. Usage of app-embedding for query expansion, nearest neighbor analysis enables unique and interesting use cases to enhance end-user experience with mobile apps.indicates equal contribution

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Section: Related Workmentioning

confidence: 99%