Tag Archives: Recommender Systems

PhD Research Proposal: Trust-aware Decentralized Recommender Systems

I realised today I didn’t write yet an entry about my PhD Research Proposal “Trust-aware Decentralized Recommender Systems” (TaDRS).
So here it is the PDF file. If you have any comment or criticism, I’ll be happy to hear from you.
The PhD research proposal is a little bit outdated (29th May 2003) but I didn’t have a blog at that time. Enjoy and let me know what you think.

This PhD thesis addresses the following problem: exploiting of trust information in order to enhance the accuracy and the user acceptance of current Recommender Systems (RS). RSs suggest to users items they will probably like. Up to now, current RSs mainly generate recommendations based on users’ opinions on items. Nowadays, with the growth of online communities, e-marketplaces, weblogs and peer-to-peer networks, a new kind of information is available: rating expressed by an user on another user (trust). We analyze current RS weaknesses and show how use of trust can overcome them. We proposed a solution about exploiting of trust into RSs and underline what experiments we will run in order to test our solution.


Reviewr “ties into the API exposed by Ludicorp’s […] new social software application, Flickr and hooks it up to the API exposed by Amazon. The point is that using Reviewr allows you to search for reviews of products by people you know and trust.” (via Hublog)
Interestingly, as I was proposing in a previous post, Friendr limits the number of contacts an user can have. It was not a totally dumb idea after all…
Check the services already created using the API and the services documentation (1, 2)

Paper submitted to iTrust2004

I submitted my paper Using Trust in Recommender Systems: an Experimental Analysis to the Second International Conference on Trust Management 2004.
You can read the PDF file or the HTML version (by latex2html).

Recommender systems (RS) have been used for suggesting items (movies, books, songs, etc.) that users might like. RSs compute a user similarity between users and use this as a weight for the users’ ratings. However they have many weaknesses, such as sparseness, cold start and vulnerability to attacks. We assert that these weaknesses can be alleviated using a Trust-aware system that takes into account the “web of trust” provided by every user.
Specifically, we analyze data from the popular Internet web site epinions.com. The dataset consists of 49290 users who expressed reviews (with rating) on items and explicitly specified their web of trust, i.e. users whose reviews they have consistently found to be valuable.
We show that users have usually few items rated in commons. For this reason, the classic RS technique is often ineffective and is not able to compute a user similarity weight for many of the users. Instead exploiting the webs of trust, it is possible to propagate trust and infer an additional weight for other users. We show how this quantity can be computed against a larger number of users.

blam! rocks

I’ve just used blam! in this review of Revolution OS.
Basically blam! add some semantic information to your blog entry when this is a review. The semantic information can be understood by a computer program so that it will be possible to, for example, aggregating all the reviews about a certain book or movie.
Read about OpenReviews and their possible uses from Accordion Guy.
I’m planning to do something similar for my project CoCoA.
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