Tag Archives: Trust and Reputation

Cai Ziegler

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During the past week I was in Oxford for the 2nd Trust Management Conference. The presentation (pdf) (sxi) of my paper went well.
Most of the participants were concerned with privacy and the problem of setting up a secure environment for virtual organizations (business basically). I am not too much interested in this topic that is basically agreeing with Microsoft, IBM and HP (that were present with some representatives) about standards for the trust management processes, often reduced to simple access control lists.

Instead I was very happy to meet Cai Ziegler. Cai is working on topics very similar to my interests. But it is doing more (his scope on semantic web recommender systems is broader, since he also takes into account taxonomies), better (its English is simply wonderful) and faster (he is still in his first year of PhD). Can I at least say I’m humble? ;-)
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PhD Research Proposal: Trust-aware Decentralized Recommender Systems

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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The PhD research proposal is a little bit outdated (29th May 2003) but I didn’t have a blog at that time.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.

UPDATE:
Abstract
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

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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)

Best social software? Orkut? No, Epinions.

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I tend to agree with Danah about Orkut. In particular, I think Orkut does not model the real social network of an user. I speak of Orkut because is the buzz of the moment and its being in affiliation with google makes it the big expectation. But the same arguments could be used against many of the social network applications listed on socialsoftwareweblog.

The question is: “Why should I not accept an invitation from a totally unknown user that pretends to be my friend?” There is no negative consequence in adding someone as friend.
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SongBuddy and Decentralized Music

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Yes, YASN (Yet Another Social Network). Have you noticed that Add as a friend is the most common link in websites created these days?
Anyway, this time we have SongBuddy. Social software + online music: an explosive pair!
SongBuddy is a new way to find music that’s already available on the Internet. By finding songs on bands’ and labels’ sites and sharing the address of those songs with your friends, you can explore music you’ll love that you wouldn’t hear anywhere else. So sign up, make some friends and list some music. You won’t even need to install any software, SongBuddy works with your current media player.
Here my profile.
SongBuddy also produces a FOAF file representing your friends and uses the MusicBrainz RDF namespace.
The term of service is also very good!
Unless otherwise specified, all content on this site is copyright SongBuddy LLC. You may use the data on this site under the Attribution-NonCommercial-ShareAlike 1.0 Creative Commons license.

Another similar site worth mentioning is Webjay by Lucas Gonze but I haven’t had time to try it yet.

This post also appears on the open channel playlistlogging

Paper submitted to iTrust2004

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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).

Abstract:
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.