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A Framework for Evaluating Personalized Ranking Systems by Fusing Different Evaluation Measures.

, , , and . Big Data Res., (2021)

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Aligning Knowledge Base and Document Embedding Models Using Regularized Multi-Task Learning., , , , , and . ISWC (1), volume 11136 of Lecture Notes in Computer Science, page 21-37. Springer, (2018)Exploring a standardized language for describing foods using embedding techniques., , , and . IEEE BigData, page 5172-5176. IEEE, (2019)Mix and Rank: A Framework for Benchmarking Recommender Systems., , and . IEEE BigData, page 3717-3726. IEEE, (2019)Random Walk TripleRush: Asynchronous Graph Querying and Sampling., , , and . WWW, page 1034-1044. ACM, (2015)Blockbusters and Wallflowers: Accurate, Diverse, and Scalable Recommendations with Random Walks., , , and . RecSys, page 163-170. ACM, (2015)A Framework for Evaluating Personalized Ranking Systems by Fusing Different Evaluation Measures., , , and . Big Data Res., (2021)Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning., , , , , , , and . WWW, page 2366-2377. ACM, (2019)Fewer Flops at the Top: Accuracy, Diversity, and Regularization in Two-Class Collaborative Filtering., , and . RecSys, page 215-223. ACM, (2017)Interaction Embeddings for Prediction and Explanation in Knowledge Graphs., , , , and . WSDM, page 96-104. ACM, (2019)Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks., and . WWW, page 2046-2057. ACM / IW3C2, (2021)