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Are User-Generated Item Reviews Actually Beneficial for Recommendation?

, , and . KaRS@RecSys, volume 3294 of CEUR Workshop Proceedings, page 17-22. CEUR-WS.org, (2022)

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How Researchers Could Obtain Quick and Cheap User Feedback on their Algorithms Without Having to Operate their Own Recommender System., and . CoRR, (2022)On gossip-based information dissemination in pervasive recommender systems., , , , , and . RecSys, page 442-446. ACM, (2019)Can We Effectively Use Smart Contracts to Stipulate Time Constraints?, and . CoRR, (2022)affinity: A System for Latent User Similarity Comparison on Texting Data., , , , , and . CoRR, (2019)Affinity: A System for Latent User Similarity Comparison on Texting Data., , , and . ICC, page 1-7. IEEE, (2019)Collaborating with Users in Proximity for Decentralized Mobile Recommender Systems., and . SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI, page 1192-1197. IEEE, (2019)Reviews Are Gold!? On the Link between Item Reviews and Item Preferences (Long paper).. KaRS/ComplexRec@RecSys, volume 2960 of CEUR Workshop Proceedings, CEUR-WS.org, (2021)The Corpus Replication Task.. CoRR, (2018)Are User-Generated Item Reviews Actually Beneficial for Recommendation?, , and . KaRS@RecSys, volume 3294 of CEUR Workshop Proceedings, page 17-22. CEUR-WS.org, (2022)Distributed Data Minimization for Decentralized Collaborative Filtering Systems., and . ICDCN, page 140-149. ACM, (2023)