“Evaluation of Algorithms for Interaction-Sparse Recommendations: Neural Networks don’t Always Win“

Authors: Yasamin Klingler, Claude Lehmann, João Pedro Monteiro, Carlo Saladin, Abraham Bernstein, Kurt Stockinger

Abstract: “In recent years, top-K recommender systems with implicit feedback data gained interest in many real world business scenarios. In particular, neural networks have shown promising results on these tasks. However, while traditional recommender systems are built on datasets with frequent user interactions, insurance recommenders often have access to a very limited amount of user interactions, as people only buy a few insurance products. In this paper, we shed new light on the problem of top-K recommendations for interaction-sparse recommender problems.
In particular, we analyze six different recommender algorithms, namely a popularity-based baseline and compare it against two matrix factorization methods (SVD++, ALS), one neural network approach (JCA) and two combinations of neural network and factorization machine approaches (DeepFM, NeuFM). We evaluate these algorithms on six different interaction-sparse datasets and one dataset with a less sparse interaction pattern to elucidate the unique behavior of interaction-sparse datasets.
In our experimental evaluation based on real-world insurance data, we demonstrate that DeepFM shows the best performance followed by JCA and SVD++, which indicates that neural network approaches are the dominant technologies. However, for the remaining five datasets we observe a different pattern. Overall, the matrix factorization method SVD++ is the winner. Surprisingly, the simple popularity-based approach comes out second followed by the neural network approach JCA. In summary, our experimental evaluation for interaction-sparse datasets demonstrates that in general matrix factorization methods outperform neural network approaches. As a consequence, traditional well-established methods should be part of the portfolio of algorithms to solve real-world interaction-sparse recommender problems.”

The paper can be read here.

The first publication is a journal article published in the distinguished Digital Journalism journal. Written as a collaboration between computer science and communication science scholars, the article studies the effects of diverse news recommender systems in the context of democracy.

“Benefits of Diverse News Recommendations for Democracy: A User Study“

Authors: Lucien Heitz, Juliane A. Lischka, Alena Birrer, Bibek Paudel (former DDIS member, currently at Stanford University), Suzanne Tolmeijer (former DDIS member, soon at the University of Hamburg), Laura Laugwitz, and Abraham Bernstein

Abstract: “News recommender systems provide a technological architecture that helps shaping public discourse. Following a normative approach to news recommender system design, we test utility and external effects of a diversity-aware news recommender algorithm. In an experimental study using a custom-built news app, we show that diversity-optimized recommendations (1) perform similar to methods optimizing for user preferences regarding user utility, (2) that diverse news recommendations are related to a higher tolerance for opposing views, especially for politically conservative users, and (3) that diverse news recommender systems may nudge users towards preferring news with differing or even opposing views. We conclude that diverse news recommendations can have a depolarizing capacity for democratic societies.”

You can read the entire article here.

The second publication is the book “Spotlight on Artificial Intelligence and Freedom of Expression: A Policy Manual“, co-authored by Eliska Pirkova, Matthias Kettemann, Marlena Wisniak, Martin Scheinin, Emmi Bevensee, Katie Pentney, Lorna Woods, Lucien Heitz, Bojana Kostic, Krisztina Rozgonyi, Holli Sargeant, Julia Haas, and Vladan Joler. You can read the full book here.

The twelfth ACM Recommender Systems Conference (RecSys) Sys is being held in Vancouver, Canada from October 2-7. According to ACM, RecSys 2018 is “the most important annual conference for the presentation and discussion of recommender systems research.” RecSys will bring together the main international research groups working in recommender systems, along with many of the world’s leading e-commerce and media companies.