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/

Rise of the Netflix Hackers

Confidentiality is still a big issue in recommendation. If you seriously think about it, the more they know about you, the better their recommendation will be. But the question is how much confidential informations one is willing to spare?

This article by Dave Demerjian tells how hackers are constantly working not only to gain more understanding of the systems but also how to compromise them.

http://www.wired.com/science/discoveries/news/2007/03/72963

Next Recommender Systems Conference

“The race to create a 'smart' Google”

Fortune magazine writer Jeffrey M. O'Brien, writes:

The Web, they say, is leaving the era of search and entering one of discovery. What's the difference? Search is what you do when you're looking for something. Discovery is when something wonderful that you didn't know existed, or didn't know how to ask for, finds you.



http://hci.epfl.ch/recsys08/

one to one Marketing

Personalized marketing (also called personalization, and sometimes called one-to-one marketing) is an extreme form of product differentiation. Whereas product differentiation tries to differentiate a product from competing ones, personalization tries to make a unique product offering for each customer.
Personalized marketing had been most practical in interactive media such as the internet. A web site can track a customer's interests and make suggestions for the future. Many sites help customers make choices by organizing information and prioritizing it based on the individual's liking. In some cases, the product itself can be customized using a configuration system.
More recently, personalized marketing has become practical with bricks and mortar retailers. The market size, an order of magnitude greater than that of the Internet, demanded a different technological approach now available and in use. Many retailers attract customers to the physical store by offering discounted items which are automatically selected to appeal to the individual recipient. The interactivity occurs through the offer redemptions recorded by the point of sale systems, which can then update each model of the individual shopper. Personalization can be more accurate when based solely upon individual purchasing records because of the simplified and repetitive nature of some bricks and mortar retail purchasing, for example grocery superstores.
Don Peppers and Martha Rogers, in their ground breaking book on the subject (Peppers, D. and Rogers, M. 1993) speak of managing customers rather than products, differentiating customers not just products, measuring share of customer not share of market, and developing economies of scope rather than economies of scale. They also describe personalized marketing as a four phase process: identifying potential customers; determining their needs and their lifetime value to the company; interact with customers so as to learn about them; and customize products, services, and communications to individual customers.
Some commentators (including Peppers and Rogers) use the term "one-to-one marketing" which has been misunderstood by some. Seldom is there just one individual on either side of the transaction. Buyer decision processes often involve several people, as do the marketer's efforts. However, the excellent metaphor refers to the objective of a single message source (store) "to" the single recipient (household), a technological analogy to a "mom and pop" store on a first name basis with 10 million customers.

References
Peppers, D. and Rogers, M. (1993) The one to one future : Building relationships one customer at a time, Doubleday (Currency Books), New York, 1993 ISBN 0-385-42528-7
Retrieved from "http://en.wikipedia.org/wiki/Personalized_marketing"

This will be fun.

Give me a break. I am just setting up.

See you soon and check pretty often.