Tony Abou-Assaleh

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.

CNN Money has an excellent articles where they interview 22 people from political leaders, company executives, investors, entrepreneurs, and even a chef, asking them to share the best advice they ever got. Below is a summary of their answers:

• “Keep it Simple” – Tiger Woods
• “Show, don’t tell” – Jim Sinegal
• “Do what you love” – Mort Zuckerman
• “Empower a subordinate” – Lloyd Blankfein
• “Push beyond your comfort zone” – Mohamed El-Erian
• “Ignore conventional wisdom” – David Axelrod
• “Trust your instincts” – Tory Burch
• “Read everything” – Jim Rogers
• “Be effective, not popular” – Scott Boras
• “Use failure to motivate yourself” – Mika Brzezinski
• “Focus on performance, not power” – Colin Powell
• “Take advice from smart people” – Shai Agassi
• “Make an impression” – Sukhinder Singh Cassidy
• “Hire a coach” – Eric Schmidt
• “Set realistic goals” – Meredith Whitney
• “Listen” – Lauren Zalaznick
• “Don’t talk shop” – Julian Robertson
• “Treat it like it’s yours” – Thomas Keller
• “Underpromise and overdeliver” – Robin Li
• “Don’t pursue titles and dollars” – Miles White
• “Self-doubt is normal” – Aaron Patzer
• “Be nice to people” – Niklas Savander

My favourite three from the list are “take advice from smart people”, “Keep it Simple”, and “Show, don’t tell.” Each of these three advices I have come to experience as being true wisdom and highly effective in a diversity of situations.

To read more about these advice, the context, and where they came from, see the original article: Best advice I ever got.