Challenge on Context-aware Movie Recommendation

The winners of CAMRa were announced during the workshop, they were:

Overall winner: Yue Shi, Martha Larson and Alan Hanjalic. Mining Mood-specific Movie Similarity with Matrix Factorization for Context-aware Recommendation

Weekly track winner: Zeno Gantner, Steffen Rendle and Lars Schmidt-Thieme. Time vs. Tags: Factorization Models for Context-/Time-Aware Movie Recommendations

Mood track winner: Yue Shi, Martha Larson and Alan Hanjalic. Mining Mood-specific Movie Similarity with Matrix Factorization for Context-aware Recommendation

Social track winner: Nathan Liu.  Adapting Neighborhood and Matrix Factorization Models for Context Aware Recommendations

Live track winner: Zeno Gantner, Steffen Rendle and Lars Schmidt-Thieme. Time vs. Tags: Factorization Models for Context-/Time-Aware Movie Recommendations

Thanks to all of you participated!

During the CARS workshop someone was asking about a mailing list on context-awareness.

Gedas Adomavicius said anyone interested in joining the list should send him an email at gedas [at] umn [dot] edu

We’ll perform the live valuation of the CAMRA challenge today. Users who received a DVD our participants recommended to them will fill out a survey today, where they tell us if they liked that recommendation or not.

We’re planning to ask these questions:

 
We’ll send out the survey this afternoon and would love to get your feedback before it goes out.

Which questions are we missing? Do we ask the right questions? Please comment!

CAMRa will be held in room A3 at the WTC in Barcelona.

A map is available here, click “A Rooms” for an overview.

We are pleased to announce that Francesco Ricci will give a keynote at CAMRa.

The Context of a Recommendation

Abstract: Recommender Systems are software tools and techniques providing suggestions for items to be of use to a user. Context is providing information that can influence the perception of the usefulness of an item for a user. For this reason Recommender Systems must take into account this information to deliver more useful (perceived) recommendations. There are several examples motivating the importance of context for Recommender Systems. For instance, to suggest a meaningful travel to a user a Recommender System must know if the travel is scheduled in summer or winter, and if the user is traveling alone or with kids. Moreover, only considering the context of an evaluation – the user rating for an item – one can assign a proper meaning to that. For instance while one can judge a Ferrari a 5 star car; this does not mean that it is a proper recommendation if the user is looking for a new car to buy. Context modeling and context-dependent reasoning is a complex subject and still there are major technical and practical difficulties to solve: obtain sufficient and reliable data describing the user preferences in context; selecting the right context information, i.e., relevant in a particular personalization task; understanding the impact of the contextual dimensions on the personalization process; embedding the contextual dimensions in a recommendation computational model. Besides, Recommender Systems research should consider the wider scope of contextual computing, defined as the enhancement of the user’s interactions by understanding the user, the context, and the applications and information being used, typically across a wide set of user goals. Actively adapting the computational environment – for each user – at each point of computation should be the ultimate goal. So, contextual computing for Recommender Systems must also focus on understanding the information consumption patterns of each user, and on the process not only on the output of the recommendation process.

Due to the large number of requests, we have decided to postpone the deadline till Monday July 5th 2010.

Submission is now open!

Submit you papers here

We are pleased to announce that the proceedings of CAMRa2010 will be published as a volume of the ACM International Proceedings Series.

We are pleased to announce that there will be a special issue ACM journal following the challenge. Authors of high-quality challenge papers will be invited to submit extended versions of their work.

We will be posting a CFP when all details have been sorted out.

The RecSys 2010 website now has a listing of all accepted workshops, link.