Innovations in Science and Technologies 1-tom 1-san (2024) · 91-97-betler

MULTIMODAL RECOMMENDATION SYSTEMS: ENHANCING RECOMMENDATION QUALITY THROUGH DATA FUSION

Yuldashov, Qudrat, Kutlimuratov, Alpamis

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Annotaciya

In today's digital age, users interact with platforms using varied content types—text, images, audio, and even video. Capturing the essence of this diverse data for recommendation can yield richer user profiles and more relevant suggestions. This paper investigates the potential of Multimodal Recommendation Systems, which integrate information from multiple sources to enhance recommendation quality. We delve into methods for data fusion, the challenges of multimodal systems, and their application across different sectors.

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Dáyeksóz alıw

APA 7 
Yuldashov, Qudrat & Kutlimuratov, Alpamis (2024). MULTIMODAL RECOMMENDATION SYSTEMS: ENHANCING RECOMMENDATION QUALITY THROUGH DATA FUSION. Innovations in Science and Technologies, 1(1), 91-97.
GOST R 7.0.5 
Yuldashov, Qudrat, Kutlimuratov, Alpamis MULTIMODAL RECOMMENDATION SYSTEMS: ENHANCING RECOMMENDATION QUALITY THROUGH DATA FUSION // Innovations in Science and Technologies. 2024. Т. 1. № 1. С. 91-97.
BibTeX 
@article{qudrat2024,
  author  = {Yuldashov, Qudrat and Kutlimuratov, Alpamis},
  title   = {MULTIMODAL RECOMMENDATION SYSTEMS: ENHANCING RECOMMENDATION QUALITY THROUGH DATA FUSION},
  journal = {Innovations in Science and Technologies},
  year    = {2024},
  volume  = {1},
  number  = {1},
  pages   = {91-97}
}
RIS 
TY  - JOUR
AU  - Yuldashov, Qudrat
AU  - Kutlimuratov, Alpamis
TI  - MULTIMODAL RECOMMENDATION SYSTEMS: ENHANCING RECOMMENDATION QUALITY THROUGH DATA FUSION
JO  - Innovations in Science and Technologies
PY  - 2024
VL  - 1
IS  - 1
SP  - 91
EP  - 97
ER  -