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Long and Short Term User Preference Learning for Next POI Recommendation in deep learning

I need implementation in python or Matlab using a neural network...

Given a comprehensive recommendation system, which has the aptitude to learn users’ long

and short-term preference for the next Point of Interest recommendation.

• Evaluate the model performance for both cold and non-cold start users together to overcome the data sparsity problem arise due to cold-start users.

• Cold-start users are those with little historic data i.e. having little check-in records.

• The probability estimation heavily relies on other users (non-cold-start users).

• A combined technique will be used in this work to understand user check-in behavior at

certain place and time learning contextual features of (POIs) and to mine long and short

term user preference together [6].

• Both these long and short-term behavior are necessary for capturing user preference for the next POI.

in mostly paper data set takes from

1 [login to view URL]

2 [login to view URL]

3 [login to view URL]

i recommended .gowalla dataset

i want to do this task for my Theses my last accuracy is 80% now i want to improve it to more 3 to 7 persentage

Evner: Deep Learning, Neural Networks, Python, Matlab and Mathematica, Machine Learning (ML)

About the Client:
( 0 bedømmelser ) BASIRPUR, Pakistan

Projekt ID: #32694526

8 freelancere byder i gennemsnit $136 timen for dette job

sajjadtaghvaeifr

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sramireddy065

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KolaPeters

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rizwanmpec

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demvitalii3

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JAYJAY692

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