Tony Abou-Assaleh

How twitter and facebook almost killed my blog

Preface

It’s been about 6 months since I last wrote on this site. And instead of writing about something useful, here I am writing about writing and not writing .. the boring useless stuff that few would care about. But wait, isn’t that the reason why I didn’t write for so long in the first place? Wasn’t it because I didn’t want to write about boring useless stuff that few would care about? C’est la vie. Life must go on, and for a site to maintain its livelihood, it’s got to be updated once in a while.

Twitter (Almost) Killed My Blog

But another more realistic reason for not writing this long is the new social media. I’ve been writing on twitter and facebook many times a day. Most of what I wanted to say I could squeeze in 140 characters, including a link, a couple hash tags, exclamation marks, and space to spare for retweets. Now that’s efficiency! But if I can be that efficient on Twitter and Facebook, and I feel I have something worthy to say there, then why not here, on my very own personal blog? Aren’t these condensed, super-efficient revelations worthy of “the Web”? Is there a minimum length requirement for blog analogous to the maximum length requirement of a tweet? Is blogging, then, the opposite of tweeting?

Conclusion

Long story short, this story is already too long. What I wanted to say was “I’m back to blog again.” Period.

The Twitter Times is an interesting news site that is built on top of the Twitter real-time stream. The site describes itself as:

“The Twitter Times is a real-time personalized newspaper generated from your Twitter account.”

It basically monitors the global real-time Twitter stream, identifies news stories that are tweeted and re-tweeted, cross-references them with you friends and their friends, and brings them all in one, well-organized place. The best part of it all, you get the all the news stories on one page – no visiting multiple sites, no following links to see what the tweet is about.

The Twitter Times is a time saver for me. I follow almost 200 streams on twitter. There is no way I can read them all. Some of these streams are of my friends, and I’d like to read them all; others I follow for the news they post, which I can now read on the twittertim.es.

I had the pleasure of meeting one the creators (@mariagrineva) and speaking with another (@maxgrinev). Congrats Maria and Maxim!

My paper entitled “Personalization of Content Ranking in the Context of Local Search” has been accepted for publication at the IEEE/WIC/ACM International Conference on Web Intelligence (WI2009) as a regular paper. The acceptance rate was pretty low at 16%. The conference will be co-located with the Intelligent Agent Technology Conference (IAT2009) in Milan, Italy. My colleague and co-author Philip O’Brien and I will be presenting the paper. The other co-authors are Xiao Luo, Weizheng Gao, and Shujie Li. The conference will be held in September 15-18, 2009 at Università degli Studi di Milano Bicocca.

If you’re planning to attend WI/AIT 2009 and would like to connect, contact me.

The abstract of the paper follows:

Ranking search results using a single ranking function for all search engine visitors is inherently bounded in the performance the ranking algorithm can achieve when considering the variety of requirements of Web searchers and the proliferation of topics and types of data modern search engines rank. Adding a geographical dimension to the mix by way of local search engines further reduces the average satisfaction a ranking algorithm can garner from local search users. Personalization has been proposed in Web search with some success but has not, to our knowledge, been investigated thoroughly in local search. As initial steps in local search personalization, we propose a model for personalizing search results in a local search engine using a hybrid of profile- and click-based user modeling methods. User profiles are used to compare local search results to the topical interests of users and the specific businesses in which they have shown interest by way of search result “clicks”. Our model is tested through a user study and is shown to result in significantly improved mean average precision over the baseline ranking system.

Although experiments were conducted in the context of Local Search, the framework for modelling user interests is transferable to any domain where semantic similarity between users or user and objects is of interest. If you have any questions or comments about our approach, I’d be happy to discuss it in person or electronically – just give me a shout!

I would like to thank the staff at GenieKnows.com for their assistance and feedback both during the experiments and during writing the paper. Special shouts go to Stephanie Armsworthy, Jason Hines, and Jacek Wolkowicz, and Tapajyoti Das. Additional acknowledgements will appear in the paper.

It took a while to get everything sorted out and in order, but now it is official. GenieKnows is sponsoring Stephen and me at this year’s Cannonball Run One Lap of America. It gets even better, GenieKnows is now an official sponsor the event. The press release came out earlier today from Mark Harper, VP of Strategic Relations. The press release is reproduced below.
Continue reading ‘GenieKnows Sponsors the Cannonball One Lap of America Race’