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Climbing the app wall: enabling mobile app discovery through context-aware recommendations.

, , , and . CIKM, page 2527-2530. ACM, (2012)

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Online Context-Dependent Clustering in Recommendations based on Exploration-Exploitation Algorithms., , , and . CoRR, (2016)Simulation and Validation of an Integrated Markets Model., , , and . J. Artificial Societies and Social Simulation, (2003)Recurrent Neural Networks with Top-k Gains for Session-based Recommendations., and . CIKM, page 843-852. ACM, (2018)Towards a Universal Neural Network Encoder for Time Series., , and . CCIA, volume 308 of Frontiers in Artificial Intelligence and Applications, page 120-129. IOS Press, (2018)Text Clustering with String Kernels in R., and . GfKl, page 91-98. Springer, (2006)Getting Deep Recommenders Fit: Bloom Embeddings for Sparse Binary Input/Output Networks., and . RecSys, page 279-287. ACM, (2017)CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering., , , , , and . RecSys, page 139-146. ACM, (2012)User-Item Reciprocity in Recommender Systems: Incentivizing the Crowd., , , , , , , and . UMAP Workshops, volume 1181 of CEUR Workshop Proceedings, CEUR-WS.org, (2014)Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning., , , , and . WSDM, page 957-965. ACM, (2022)RecSys'16 Workshop on Deep Learning for Recommender Systems (DLRS)., , , , , , and . RecSys, page 415-416. ACM, (2016)