Posts Tagged ‘recsys’

forgetting for recsys

Sunday, September 26th, 2010

Before the digital age, remembering was costly and hard, and the default for humans was to forget. Forgetting is a good thing for a society, not least because people are willing to engage (they don’t fear the recall of trivial past deeds) and take better decisions (forgetting allows human decision-making to generalise and abstract from individual experiences). In the digital age, the balance has been inverted: remembering is cheaper and easier than forgetting. I was thinking about possible technological solutions that make forgetting a tiny bit easier than remembering in the digital world. So my question is: what if you had a recommender system in which you could specify the expiration date for each of your ratings? What would you do? Recommender system for online shopping might be a case in point (“past online purchases” are used to recommend items you might like). Forgetting might be useful for users who order birthday presents in that

they accutane 10mg might opt for  short expiration dates for those  purchases,  so future  recommendations for them would not be influenced by their purchases for somebody else. Any other practical idea is welcome!!! ;-)

Tutorials at RecSys 2008

Friday, October 24th, 2008

Yesterday was the first day of RecSys 2008, and was dedicated to three very interesting tutorials:

1. Robust Recommender Systems. Robin Burke introduced the wide range of attacks that typical collaborative filtering algorithms are vulnerable to; scenarios that arise when people attempt to force, rather than express, opinions. An attack was strictly defined as a set of profiles intending to obtain excessive influence on others, which can be aimed at pushing (making recommendation more likely) or nuking (i.e. recommendation less likely) items. His talk was an interesting blend of attack strategies, knowledge that attackers need to have, and a high-level description of approaches aiming at preventing or fixing the system when attacked. Of course, there are strong overlaps between this work and work in other areas (p2p trust, adversarial information retrieval, search engine spam..); I particularly like this area as pushes the point that recommender systems are about people/dynamic datasets, and not just prediction.

2. Recent Progress in Collaborative Filtering. Yehuda Koren (who has recently moved from AT&T to Yahoo! Research) gave a tutorial about the leading approaches in the Netflix prize competition. The techniques he described blend matrix factorisation and neighbourhood models, and include a number of other factors (such as user biases and time) that result in techniques that have multiple-billions of parameters (and the resulting ranking of team BellKor in the competition). His work is remarkable and worthy of the progress prizes he has been awarded thus far. He also explored alternative techniques of evaluating recommender systems, explaining his take on evaluating top-N recommendation lists.

3. Context-Aware Recommendations. Gedas Adomavicius and Alex Tuzhilin introduced their work on incorporating context into recommender systems, including pre-, post-, and hybrid-filtering of recommendation algorithm results based on user context. A running example that was repeated throughout the tutorial was going to the theatre with your girlfriend on the weekend: if you always watch comedy, then your recommendations can be filtered to match what you did in previous instances of the same context (i.e. you can be recommended comedy). They have done a lot of cool stuff on multi-dimensional recommenders, extending the common rating scales into cubes of ratings, and stressed more than once that this is virgin territory. Their work is also impressive, but raised a few questions. For example, should context be described by a well-enumerated taxonomy? Moreover, if you always watch comedy at the theatre with your girlfriend on weekends, then why should you need a recommender system in the first place (especially a collaborative one- what happened to serendipity or diversity)? They have a number of papers that are worth reading before trying to answer these questions!