Intelligent Product Retrieval System

Authors

  • Sharma M Department of Computer Science, East West Institute of Technology, Bengaluru, India
  • Pavani S Department of Computer Science, East West Institute of Technology, Bengaluru, India
  • Pooja R Department of Computer Science, East West Institute of Technology, Bengaluru, India
  • Pooja R Department of Computer Science, East West Institute of Technology, Bengaluru, India
  • Sunanda V K Department of Computer Science, East West Institute of Technology, Bengaluru, India

Keywords:

TV-to-Online, distance metric learning, transfer learning, heterogeneous domain, manifold regularizatio, , ranking- based loss

Abstract

It is desired (especially for young people) to shop for the same or similar products shown in the multimedia contents (such as online TV programs). This indicates an urgent demand for improving the experience of TV-to-Online (T2O). In this paper, a transfer learning approach as well as a prototype system for effortless T2O experience is developed. In this paper, a novel manifold regularized heterogeneous multitask metric learning framework is proposed, in which each domain is treated equally. The proposed approach allows us to simultaneously exploit the information from other domains and the unlabelled. In the system, a key component is high-precision product search, which is to fulfil exact matching between a query item and the database ones. The matching performance primarily relies on distance estimation, but the data characteristics cannot be well modelled and exploited by a simple Euclidean distance.

References

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Published

2025-11-26

How to Cite

[1]
M. Sharma, S. Pavani, R. Pooja, R. Pooja, and V. K. Sunanda, “Intelligent Product Retrieval System”, Int. J. Comp. Sci. Eng., vol. 7, no. 15, pp. 128–132, Nov. 2025.

Issue

Vol. 7 No. 15 (2019): IJCSE Special Issue May Edition

Section

Research Article

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