Archive for the ‘sharing’ Category

Private Collaborative Filtering

Thursday, June 7th, 2007

Recently, I submitted my first paper to RecSys 07, “Private Distributed Collaborative Filtering using Estimated Concordance Measures.” Even though it is not particularly about mobile-stuff, here’s a quick run through the main ideas:

Collaborative filtering is a means of using a community’s behavior, within a certain domain (movies, music), to support reducing the amount of information that each individual needs to looks through to find their items of interest. It is the dominant method behind recommender systems (such as Amazon, etc), and is based on a simple idea: people with previous shared interests will, most likely, share common likes and dislikes in the future. So, to predict how much I will like a certain item, the system first compares my rating history to all the other users to produce similarity measures, and then uses these similarity measures to compute a weighted average of how much they enjoyed the item in question.

The problem is that this method allows for no privacy. Particularly in a distributed environment, where I do not know how much to trust unknown neighbors, I do not want to have to share my entire rating history (i.e. my profile) with them, to find a similarity measure- thus discouraging cooperation, which is harmful to collaborative filtering. The actual similarity measures (the most famous being the Pearson correlation coefficient), simply cannot be found without full profile disclosure.

Therefore, in the paper we proposed a new similarity measure, based on concordance. You and I rate a movie concordantly if we both rate it above or below our mean rating (in other words, we agree about whether to give it thumbs up/thumbs down). If you hated the movie (and rated it below your mean), and I loved it (rating it above my mean), then we disagree- the ratings are discordant. If one of us has no opinion about the movie, we just say it is tied. The new similarity measure is derived from the number of concordant (C), discordant (D), tied (T) pairs between our rating sets, and the size of the set N, and this similarity measure works just as effectively (in terms of generating recommendations) as the Pearson correlation coefficient.

So how can this be used to include privacy in collaborative filtering? If you and I share a common randomly-generated set, and report to each other the number of C, D, and T pairs we have with the random set to each other, these values can be used to place bounds on the actual values of C, D, and T pairs we have with each other: we can estimate our similarity without ever sharing any profile-specific information, only sharing abstracted profile information derived from a comparison with a random set. Privacy is not breached, and, along with an incremental learning technique (future work) about how to evolve the similarity measure between recommenders, we can start collaborating with each other!

If you’re interested in the details (and the evaluation), I’ll post the paper on my web site soon (when I hear back from RecSys!)

MObilize and SHare (Mosh) by Nokia

Wednesday, June 6th, 2007

Nokia enters Social Sharing World with Mosh. MOSH is a content sharing site where community members upload, distribute and manage content to be viewed and enjoyed on mobile devices. With MOSH, anything from applications like mobile games, to videos, blogs, songs or photos are now accessible and distributable on your mobile device.

There are three key elements to MOSH:
1. A website
2. A mobile website
3. An application for mobile devices (available for download on Nokia devices only)

The website is your main source for accessing the wide range of content available through MOSH. It is here where you can create your profile, upload content, manage your collections and specify which selects to send to your mobile device as mobile feeds.

BitTyrant: a selfish BitTorrent client that improves performance

Friday, January 5th, 2007

BitTyrant is a BitTorrent client with a novel unchoking algorithm.

Suppose your upload capacity is 50 KBps. If you’ve unchoked 5 peers, existing clients will send each peer 10 KBps, independent of the rate each is sending to you. In contrast, BitTyrant will rank all peers by their receive / sent ratios, preferentially unchoking those peers with high ratios.

During evaluation testing on more than 100 real BitTorrent swarms, BitTyrant provided an average 70% download performance increase when compared to the existing Azureus 2.5 implementation, with some downloads finishing more than three times as quickly.

I wonder how well it performs in swarms of other BitTyrant clients?

The USENIX paper is here.

Update: it seems to be using the same faster than the bear algorithm I came up with last year. Damn it, I should have tried it out. :-)

Identify objects viewed on the screen of a camera phone

Tuesday, November 21st, 2006

From TechReview. A Nokia research project could one day make it easier to navigate the real world by superimposing virtual information on an image of your surroundings. The new software, called Mobile Augmented Reality Applications (MARA), is designed to identify objects viewed on the screen of a camera phone.

The Nokia research team has demonstrated a prototype phone equipped with MARA software and the appropriate hardware: a global positioning system (GPS), an accelerometer, and a compass. The souped-up phone is able to identify restaurants, hotels, and landmarks and provide Web links and basic information about these objects on the phone’s screen. In addition, says David Murphy, an engineer at Nokia Research Center, in Helsinki, Finland, who works on the project, the system can also be used to find nearby friends who have phones with GPS and the appropriate software.

Recommendation Software

Tuesday, November 14th, 2006

US online DVD rental service Netflix Inc has announced a version of the Longitude prize for film geeks – a $1m (£529.6m) bounty to the first person to develop software to improve its movie recommendation system by 10%.
The full story is posted in the Guardian and on the NetFlix Prize site

The current system works by making predictions based on correlations between user feedback (as described in this interview).


Friday, November 10th, 2006

The program of ACM MobiShare (International Workshop on Decentralized Resource Sharing in Mobile Computing and Networking) – in conjunction with Mobicom.