Github just announced their own version of the Netflix Prize. Instead of predicting movie ratings, Github wants you to suggest repositories for users to watch. This is different from the Netflix Prize in a number of ways: a user watching a repo is similar to a user visiting a page from a search engine – [...]
Posts Tagged ‘recommender systems’
Github announces recommender system contest
Posted: 30 July 2009 in UncategorizedTags: contests, github, netflix prize, recommender systems
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Netflix Prize just about wrapped up
Posted: 2 July 2009 in UncategorizedTags: clerk dogs, cmu, collaborative filtering, discovery engines, graduate school, hcir, human computer information retrieval, machine learning, movies, netflix, netflix prize, recommender systems, research
Image via CrunchBase It looks like some of the top players in the Netflix Prize competition have teamed up and finally broke the 10% improvement barrier. I know I’m a few days late on this, though not because I didn’t see when it happened. I’ve been battling an ear infection all week and it has [...]
clerk dogs
Posted: 18 December 2008 in UncategorizedTags: brazil, clerk dogs, dark comedy, jinni, movie genome project, movie recommendations, movies, netflix, recommender systems, sci-fi
I happened on clerk dogs, a new movie recommender, the other day. They are still in beta and are missing data in many key areas of film, but they are definitely worth checking out. Like Pandora, clerk dogs uses human editors to classify movies along several dimensions. Indeed, the founder Stuart Skorman (also founder of [...]
Stacked Agents Model
Posted: 3 July 2008 in UncategorizedTags: cmu, collaborative filtering, computational linguistics, information retrieval, machine learning, presentations, recommender systems, research
This is research I did a while ago and presented Monday to fulfill the requirements of my Masters degree. The presentation only needed to be about 20 minutes, so it was a very short intro. We have moved on since then, so when I say future work, I really mean future work. The post is [...]
The limits of collaborative filtering?
Posted: 25 June 2008 in UncategorizedTags: attributes, collaborative filtering, logic, machine learning, netflix prize, proportional analogies, recommender systems, relations, similarity
Peter Turney posted recently on the logic of attributional and relational similarity. Attributes are features or characteristics of a single entity. Relations describe some connection between two entities, such as a comparison. We’ll denote a relation between two entities A and B as A:B. A relational similarity between two groups A, B and C,D will [...]
MT Eval with Binary Comparisons
Posted: 12 May 2008 in UncategorizedTags: collaborative filtering, computational linguistics, machine learning, machine translation, machine translation evaluation, mt, mt eval, rankings, recommender systems
The standard way of doing human evaluations of machine translation (MT) quality for the past few years has been to have human judges grade each sentence of MT output against a reference translation on measures of adequacy and fluency. Adequacy is the level at which the translation conveys the information contained in the original (source [...]
SIGKDD Workshop on Large Scale Recommender Systems
Posted: 7 April 2008 in UncategorizedTags: cfp, conferences, netflix prize, recommender systems, sigkdd
The second workshop on large scale recommender systems will be at SIGKDD in Las Vegas this year. One of the main topics is the Netflix competition and Jim Bennett of Netflix is one of the co-chairs, so there should be some interesting stuff in that area. Plus all the other cool stuff going on with [...]
Ensembles of kNN Recommenders
Posted: 1 April 2008 in UncategorizedTags: cmu, ensemble methods, ensembles, information retrieval, kdd, kdd cup, knn, machine learning, netflix prize, recommender systems, rmse
I’ve been messing around with recommender systems for the past year and a half, but not using the kNN (k-Nearest Neighbors) algorithm. However, my current homework assignment for my Information Retrieval class is to implement kNN for a subset of the Netflix Prize data. The data we are working with is about 800k ratings, which [...]
