An Architecture for Behavior-Based Library Recommender Systems

This article by Andreas Geyer- Schulz, Andreas Neumannn, and Anke Thede talk about a way to integrate recommender systems in traditional libraries.

more on this article can be found @

http://www.ala.org/ala/lita/litapublications/ital/2204geyer.cfm

Interaction with movieLens

Movielens finally put a movie I was dying to watch but never got a chance to on my list. When I started, the recommendations were pretty crappy in the sense that some of the movies recommended where very old. Now after rated almost 64 movies, they started getting a sense of my taste and preference.
This week, I've been recommended "cloverfield" which I was planning to watch. but one thing I don't like about the recommendation is that they don't tell exactly why the recommendation is a good fit. it will be very helpful to the user if they can put a little summary of the movie so the user can judge the quality of the recommendation.

Facing Uncertainty in Link Recommender Systems

This paper of Jean-Yves Delort and Bernadette Bouchon-Meunier talks about the uncertainty link recommender systems are facing.

More on the topic can be found @ http://www2002.org/CDROM/poster/63/

Dynamically Optimized Context in Recommender Systems

This paper by GhimEng Yap, AhHwee
Tan advocates context-awareness as a promising approach to enhance the performance of recommenders, and introduces a mechanism to realize this approach.

more on this can be found @ http://www.ntu.edu.sg/sce/labs/erlab/publications/papers/asahtan/context_mdm05.pdf

Movielens making good recommendation so far

In my 45 min interaction with movielens this week, I received good recommendation so far. There are lots of movies whose preview I have seen but did not get a chance to watch integrally. the rating movielens predicted for those movies is very close to what I expected from the previews. Some of the rating were a little off but most of them were pretty close.

I have rated 30+ movies so far but it just take too long to sort through the movies you have actually seen.

My next project is to pick one of the predicted good movie they suggested and go watch it to see if that will match my preference.

Will let you know.

Interaction Design for Recommender System

This paper by Kirsten Swearingen and Rashmi Sinha suggests the methodology for designing a good recommender. They studied 11 sites using recommender system and draw a good comparison on the approach user by each.

This is a good paper for this class as it demonstrates how to design a successful recommender system.

http://www.rashmisinha.com/articles/musicDIS.pdf

A trust-aware decentralized remcommender

Moleskiing.it is an information aggregator and adaptive web Recommender System. The high level goal of the system is to make ski mountaineering safer by exploiting information and communication technologies.

Precisely, Moleskiing is a catalogue of ski mountaineering routes in Trentino, Italy. Every route is identifiable by means of a unique URL. An user can create an identity on the moleskiing.it site and this allows her to keep an online diary (blog), her ``moleskine about skiing'' from which the site takes the name.

Source:
http://www.w3.org/2001/sw/Europe/events/foaf-galway/papers/fp/trust_aware_decentralized_recommender_system/