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Boosting collaborative filtering with an ensemble of co-trained recommenders.

, , and . Expert Syst. Appl., (2019)

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Incorporating Semantic Item Representations to Soften the Cold Start Problem., , , and . WebMedia, page 157-164. ACM, (2018)Exploiting different users' interactions for profiles enrichment in recommender systems., , , and . SAC, page 1080-1082. ACM, (2016)Exploiting multimodal interactions in recommender systems with ensemble algorithms., and . Inf. Syst., (2016)CoBaR: Confidence-Based Recommender., , and . CoRR, (2018)Pre-processing approaches for collaborative filtering based on hierarchical clustering., , , and . Inf. Sci., (2020)Case recommender: a flexible and extensible python framework for recommender systems., , , , and . RecSys, page 494-495. ACM, (2018)Boosting collaborative filtering with an ensemble of co-trained recommenders., , and . Expert Syst. Appl., (2019)Improving Personalized Ranking in Recommender Systems with Multimodal Interactions., , , and . WI-IAT (2), page 198-204. IEEE Computer Society, (2014)978-1-4799-4143-8.Evaluating Multiple User Interactions for Ranking Personalization Using Ensemble Methods., , , and . SEKE, page 697-696. KSI Research Inc. and Knowledge Systems Institute Graduate School, (2018)A personalized clustering-based approach using open linked data for search space reduction in recommender systems., , , and . WebMedia, page 409-416. ACM, (2019)