Other texts were just useless. “Go to shop X and get Y% discount with this text.” I won’t say what the shop is, let’s just leave it at the fact that its contents don’t quite fit my profile (specifically gender). Why do you keep interupting me from the book I was reading to give me this useless advertisement? My only current solution is to unsubscribe- but I’ll lose all the information I liked then!
To any reader of this blog, these ~4 text messages are reminiscent of Daniele’s work on trust models on mobile phones, and I was thinking about how it overlaps with all the work on recommender systems and collaborative filtering. Collaborative filtering, by growing out of information retrieval work, is still bound, or perhaps tangential, to search (as was discussed at RecSys); how can we provide query-less results to users based on the (possibly implicit) information they provide? To that extent, there are a lot of ways it can spill over into the mobile world- either by using the mobile phone as a mere interface to centralised servers, or profiling users in other ways, such as location, co-location, content they carry, etc (the sort of things related to Liam’s work?). As we can see from the sort of things Google is doing, people can be profiled for (nearly?) everything they do.
“Recommendations Push:” I am looking for content. Recommender systems are successful online because people want to explore the results they give. In other words, when online I am trying to pull useful recommendations from the system. Text messages being sent to me is not the same; it is Easyjet pushing recommendations my way. I have to check my phone and make sure its not a friend texting me.
“Recommender Trust:” I trust Easyjet to only send me the advertisements they think are right for me (regardless of any accuracy considerations). In this case it feels like Easyjet is more interested in sending me advertisements of shops that are in the same airport that I should be in.
This entry was posted on Thursday, December 27th, 2007 at 10:28 pm and is filed under mobile, recommendations, research, spam, trust.	You can follow any responses to this entry through the RSS 2.0 feed. Both comments and pings are currently closed.