kmule: a framework for user-based comparison of recommender algorithms

KMulE: A Framework for User-based Comparison of Recommender Algorithms Alan Said, Ernesto W. De Luca, Benjamin Kille, Brijnesh J. Jain, Immo Micus, Sahin Albayrak The KMulE User Interface DAI Lab, Technische Universität Berlin (www.dai-labor.de) KMulE KMulE is a framework for user-based comparison of movie recommendations. Recommendations are generated by a set of algorithms allowing users to pick which one fits their current mood best. Each set of recommendations is presented with a short description on how it works, e.g. whether it is based on the users, age, location, movie popularity, etc. Acknowledgments The work in this paper was conducted in the scope of the KMulE project which was sponsored by the German Federal Ministry of Economics and Technology (BMWi). The authors would like to express their gratitude to the moviepilot team for their relevant insights and support. Available algorithms ● Pearson Similarity-based k-NN (baseline) ● Age-based k-NN ● Gender-based k-NN ● Inverse popularity-based k-NN ● Location-based k-NN ● Rating distribution similarity-based k-NN ● Easily extendable to include more Interface Description Each column in the user interface shows recommendations from one recommendation algorithm allowing users to discover movies based on user-based, movie-based features or system-wide features . The recommender backend KMulE is implemented on top of an extended version of Apache Mahout’s Taste library using the Movielens 1 million dataset. The demo is running on a Core i5 laptop, memory usage per algorithm is 200-800mb. The whole system will be released as open source during 2012. Motivation Recommender systems aim at presenting the most suitable items for their users. A system will usually present one set of recommendations for their users, not taking into consideration the context of the user. KMulE presents several lists of recommendations, allowing users to select which recommendations fit their current situation best. Collecting this information will allow for more accurate context-aware recommendations in the future. Bibliography • Said, A., De Luca, E. W., and Albayrak, S. Inferring contextual user profiles - improving recommender performance. CARS ’11 (2011). • Said, A., De Luca, E. W., and Albayrak, S. Using social and pseudo social networks to improve recommendation. ITWP’11 (2011). • Said, A., Jain, B., and Albayrak, S. Analyzing weighting schemes in collaborative filtering: Cold start, post cold start and power users. ACM SAC ’12 (2012). • Said, A., Kille, B., De Luca, E. W., and Albayrak, S. Personalizing tags: a folksonomy-like approach for recommending movies. HetRec ’11 (2011). • Said, A., Plumbaum, T., De Luca, E. W., and Albayrak, S. A comparison of how demographic data affects recommendation. UMAP’11 (2011). • Said, A., Kille, B., Jain, B.J., Albayrak, S. Increasing Diversity Through Furthest Neighbor-Based Recommendation. DDR’12 (2012)

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Demo presented at IUI 2012. Collaborative filtering recommender systems come in a wide variety of variants. In this paper we present a system for visualizing and comparing recommendations provided by different collaborative recommendation algorithms. The system utilizes a set of context-aware, hybrid, and other collaborative filtering solutions in order to generate various recommendations from which its users can pick those corresponding best to their current situation (i.e. context). All user interaction is fed back to the system in order to additionally improve the quality of the recommendations. Additionally, users can explicitly ask the system to treat certain recommenders as more important than others, or disregard them completely if the list of recommended movies is not to their liking.

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KMulE: A Framework for User-based Comparison of

Recommender Algorithms

Alan Said, Ernesto W. De Luca, Benjamin Kille, Brijnesh J. Jain, Immo Micus, Sahin Albayrak

The KMulE User Interface

DAI Lab, Technische Universität Berlin (www.dai-labor.de)

KMulE KMulE is a framework for user-based comparison of movie recommendations. Recommendations are generated by a set of algorithms allowing users to pick which one fits their current mood best. Each set of recommendations is presented with a short description on how it works, e.g. whether it is based on the users, age, location, movie popularity, etc.

Acknowledgments The work in this paper was conducted in the scope of the KMulE project which was sponsored by the German Federal Ministry of Economics and Technology (BMWi). The authors would like to express their gratitude to the moviepilot team for their relevant insights and support.

Available algorithms

● Pearson Similarity-based k-NN (baseline) ● Age-based k-NN ● Gender-based k-NN ● Inverse popularity-based k-NN ● Location-based k-NN ● Rating distribution similarity-based k-NN ● Easily extendable to include more

Interface Description

Each column in the user interface shows recommendations from one recommendation algorithm allowing users to discover movies based on user-based, movie-based features or system-wide features .

The recommender backend KMulE is implemented on top of an extended version of Apache Mahout’s Taste library using the Movielens 1 million dataset. The demo is running on a Core i5 laptop, memory usage per algorithm is 200-800mb. The whole system will be released as open source during 2012.

Motivation Recommender systems aim at presenting the most suitable items for their users. A system will usually present one set of recommendations for their users, not taking into consideration the context of the user. KMulE presents several lists of recommendations, allowing users to select which recommendations fit their current situation best. Collecting this information will allow for more accurate context-aware recommendations in the future.

Bibliography

• Said, A., De Luca, E. W., and Albayrak, S. Inferring contextual user profiles - improving recommender performance. CARS ’11 (2011).

• Said, A., De Luca, E. W., and Albayrak, S. Using social and pseudo social networks to improve recommendation. ITWP’11 (2011).

• Said, A., Jain, B., and Albayrak, S. Analyzing weighting schemes in collaborative filtering: Cold start, post cold start and power users. ACM SAC ’12 (2012).

• Said, A., Kille, B., De Luca, E. W., and Albayrak, S. Personalizing tags: a folksonomy-like approach for recommending movies. HetRec ’11 (2011).

• Said, A., Plumbaum, T., De Luca, E. W., and Albayrak, S. A comparison of how demographic data affects recommendation. UMAP’11 (2011).

• Said, A., Kille, B., Jain, B.J., Albayrak, S. Increasing Diversity Through Furthest Neighbor-Based Recommendation. DDR’12 (2012)

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