My papers

This is my final papers about Personalized Seacrh

Here is my class presentation

Week - 11 Resource

Robust Collaborative Filtering is a new filtering concept that uses Robust statistics. Robust statistics is an area within statistics where estimation methods have been developed that detoriate more gracefully in the presence of unmodeled noise and slight departures from modeling assumptions. This prevents against noisy ratings that might affect the recommendation results and also different attacks (e.g. profile injection attacks, shilling attacks) through the use of M-estimators and Robust Matrix factorization. But the experimental results from this paper show that application of M-estimators does not add significant stability; modified SVD algorithms outperform RMF in robustness. However the presented algorithm can outperform existing recommendation algorithms in recommendation quality.

Week - 11 Pandora

- First by logging, “It was not easy” by Cece Winans was played from my “Cece Winans” station. I noticed that Pandora always plays the first song from the main artist in the station. I tried to find people with similar profiles as mine, but Pandora doesn’t provide this kind of features. You search for person, music station by names; Pandora should provide an option like “See matching profiles.” In here people should browse through similar users and find other interesting things. Because people expectations are malleable, unstable and vary. It might return a high degree of serendipity.

- I noticed also that Pandora did not provide a high degree of privacy from the profiles I glanced through. I don’t know if it’s users’ preferences, because it is easy to detect and locate users. Pandora should provide an option for anonymized profile.

- On more weird thing, Pandora returns songs that I’ve already rated, but unrated. I have to rate them for a second time !!!!!!. Overall my “Cece Winans” station is almost accurate.

Week - 10 Resource

Personalization: Reducing Complexity for easier, more intuitive User experience.

As digital content, and communication become richer and complex, it is indispensable to personalized user experience in mobile platform. The approach Motorola is taking is personalization. It places user in the center of interaction, application and computing activities. A vision that Motorola had since the 90's since when they initiated many research projects and developed many tools. Motorola sees three personalization emerging: user interaction personalization, content personalization, and applicatios and services personalization. Among initiatives and platforms with personalization as core concept are: Motorola' s intelligent user interaction framework, Seamless mobility initiative, Motorola SCREEN3, MobiLife platform. Motorola is also trying to develop personalization standards by working with many other groups.