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.

Week - 9 Pandora

- Pandora is getting closer and closer to my preferences in my Donnie McClurkin station. For instance, by loggging today, i got "Great and Mighty is our God" by Donnie McClurkin. I decided to perfect my Psalms station, the first song was "Peace,Love & Understanding" by Robben Ford; this song don't reflect my prefences. My next suggestion was "Can't give up now" by Mary Mary, since i added this artist in this station, i think Pandora picked it up from my profile whick is ok. My next song was "Purify" by Cece Winans.

- Pandora provides listeners with an option "Quick mix" where different stations can be mixed. Listeners can also chose music genre. It also recognizes stations based on their music genre they contain. Curiously, Pandora recognizes my Donnie McClurkin and Psalms stations as rock station. This is not true for my Donnie McClurkin, 'cause in this station i mostly flagged and liked songs related to Donnie McClurkin (is Donnie music rock?). For the Psalms station, Pandora randomnly flshes my rock and folk music though i'm trying to input as much as i can my preferences.

-

Week - 9 Resource

In this article , writer jemina Kiss argued that the speculated Web 3.0 will be about personalization. The site held a constest asking readers for their web 3.0 definitions, the favorite definition came from Robert O'Brien, Web 3.0 is defined as "decentralized asynchronous me". With the promise of semantic web, machines understand things in human terms, and can apply that knowledge to your attention data. The result we'll have a web that knows what we want and when we want it. So it is all about personalization and recommendation. Just a speculation from Jemina !!!!!! What's your idea ......

Week 8 - Resource

PRES is a recommender system that recommends links (hyperlinks) based on content-based filtering. A content-based filtering system selects items based on the correlation between the content of the items and the user’s preferences. PRES makes recommendations by
comparing a user profile with the content of each document in the collection. Term weights of document can be determined by using the tf-idf scheme. The user profile is represented with the same terms and built up by analyzing the content of documents that the user found interesting. Finally similarity measures (cosine) are used to measure how similar is a document to user profile and those (links) unred are recommended to the user.