Efficient personalized learning to rank from implicit feedback for time-sensitive recommendations

This thesis focuses on the problems at the intersection of time-sensitive recommendations, implicit user feedback, and learning to rank. Major challenges for achieving time sensitivity are distinguished, the importance of handling implicit feedback is emphasized, and an overview of learning to rank methods is presented with an emphasis on the models that can learn from implicit feedback for time-sensitive recommendations. Subsequently, novel and improved personalized learning to rank methods are proposed to handle large-scale implicit feedback datasets and streams as well as to defeat the different challenges for achieving time-sensitive recommendations. These proposals comprise: (i) Mining the user feedback stream for collaborative filtering and the SASCF algorithm, (ii) Parallel personalized pairwise learning to rank and the PLtR family of algorithms, (iii) Improving the efficiency of top-N predictions from matrix factorization models and the MMFNN meta-algorithm, (iv) Learning intention in user sessions and the BRF family of algorithms, and finally (v) Timely push recommendations in a cold start setting and a hybrid learning to rank approach. Theoretical as well as extensive empirical analyses of the proposed methods on real-life data show significant performance and trade-off improvements with respect to ranking accuracy, adaptivity, diversity, efficiency, and scalability.