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Who will you dial next?

Who will you dial next?. Assif Ziv Ohad Assulin Under the direction of Dr. Yonatan Loewenstein. Contents. ► The Idea ► The Android App. ► The Algorithm ► Research. The Idea. ► Algorithm to be used by mobile phones. The algorithm will predict which one of your

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Who will you dial next?

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  1. Who will you dial next? Assif Ziv Ohad Assulin Under the direction of Dr. Yonatan Loewenstein

  2. Contents ► The Idea ► The Android App. ► The Algorithm ► Research

  3. The Idea ► Algorithm to be used by mobile phones. The algorithm will predict which one of your contacts you will call next. ► Implementation of the algorithm as a Smartphone’s app. …Collect valuable information about people…

  4. The One Click Test Clicking many times on the Smartphone’s screen in order to make a call is uncomfortable (i.e. users are lazy). We want to be able to call the person we want, using only one click! We thought of two ways to utilize the algorithm’s output for this purpose

  5. Contacts re-ordering

  6. Contacts re-ordering Predicted Ohad Assulin Installing… Done! Assif Ziv Eli Cohen Dana Dolev Doron C A Assif Ziv AviNir B Boaz Zor

  7. Desktop widget

  8. Project Timeline App. beta release US Patent filed IBM’s DB MIT‘s DB Cognitive Science Bs.C final paper PostPC Course Info. Theory Course

  9. The Android App.

  10. Architecture

  11. Difficulties ► User Interface ► Widget’s API is limited ► Design is problematic due to the wallpaper background ► The ML algorithm is heavy ► Memory-wise ► Computationally-wise

  12. The Algorithm

  13. First attempt - v0.1

  14. v0.1 Algorithm ► Algorithm: Simple decay through time (τdependent) ► Predict (n) 1. for each contact c 1.1 Array[c] ← Pc(n) 2. return index of max(A)

  15. Our dataset

  16. Experiments ► We ran the algorithm over the dataset, and counted successful predictions ► v0.2 measured τin seconds, so we could understand what it means

  17. v0.2 Visualization τ Time

  18. More experiments ► v0.3 Dual τand α weight between them ► v0.4 Heuristic: Lower chances of calling the most frequent contact right after a call to him/her ► v0.5 Prediction by average call frequency

  19. The bottom line ► We have tested various Machine Learning Algorithms, including various Neuronal Network algorithms ► Our best results (1 year ago) averaged ~80% success rate using the 8-One-Click-Test ► Prediction of the most called contacts averages ~55% success rate

  20. The Patent ► During the Android Dev. Course Amnon Dekel introduced us to Tamir Huberman from Yissum. ► Yissum took ownership of the invention, and filed a Provisional Patent in the US. ► It was the second case in HUJI history a patent is registered for undergrad students!

  21. Research

  22. Zipf’s Law ► Named after the linguist George Kingsley Zipf (Harvard 1935) ► Describes a power-law distribution: ► Doesn’t work for incoming calls or outgoing SMS

  23. Zipf’s Law - Results Our dataset ► 6 people ► 6.4 (avg.) months ► ~7.7K calls MIT ► 40 people ► 9 months ► ~40K calls IBM ► ~1M people ► 1 month ► ~1B calls

  24. Mutual Information ► Mutual information is a quantity that measures the mutual dependence of two random variables ► We could compare input parameters by predictability ► Example: Day of the week < Hour of day

  25. Future Research ► directed graph implications ► Assessing the network’s symmetry ► Finding opinion leaders ► Rate people by popularity ► Statistical analysis of IBM’s Dataset ► How incoming calls relate to outgoing calls?

  26. Thanks ► Dr. Yonatan Loewenstein (Cognitive Science) ► Mr. Amnon Dekel (Computer Science) ► Prof. Jacob Goldenberg (Business Admin.) ► Tamir Huberman (Yissum) ► Nathan Eagle (MIT) ► IBM

  27. Discussion What could we learn from this data? ► Would we be able to categorize people? ► Could we tell whether they have a partner? ► Could we tell how old are they? ► Could we rank them socially? ► Can we predict when will a significant contact be added? What is your idea of a heuristic?

  28. Questions?

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