Hadamard product feature interactions work because they make CTR models quadratic networks, and the proposed QNN-alpha with multi-head Khatri-Rao product and self-ensemble loss achieves state-of-the-art results on six public click-through-rate datasets.
Frappe: Understanding the Usage and Perception of Mobile App Recommendations In-The-Wild
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abstract
This paper describes a real world deployment of a context-aware mobile app recommender system (RS) called Frappe. Utilizing a hybrid-approach, we conducted a large-scale app market deployment with 1000 Android users combined with a small-scale local user study involving 33 users. The resulting usage logs and subjective feedback enabled us to gather key insights into (1) context-dependent app usage and (2) the perceptions and experiences of end-users while interacting with context-aware mobile app recommendations. While Frappe performs very well based on usage-centric evaluation metrics insights from the small-scale study reveal some negative user experiences. Our results point to a number of actionable lessons learned specifically related to designing, deploying and evaluating mobile context-aware RS in-the-wild with real users.
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Revisiting Feature Interactions from the Perspective of Quadratic Neural Networks for Click-through Rate Prediction
Hadamard product feature interactions work because they make CTR models quadratic networks, and the proposed QNN-alpha with multi-head Khatri-Rao product and self-ensemble loss achieves state-of-the-art results on six public click-through-rate datasets.