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- W4385300518 abstract "Cloud APIs have always been the best carrier for service delivery, capability replication, and data output. The proliferation of cloud APIs has increased the difficulty of selecting the right, composable cloud APIs. However, very little research has attempted to address long-tail API recommendation or complementary API recommendation. In this paper, we focus on this shortcoming and propose Complementary LongTail API Recommendation (CLAR). To address the problem, we propose an attribute-aware recommender system, Neural Complementary Recommendation (NCR). Specifically, we use multi-modality embedding, including self-embedding and global embedding, to perform feature transformation. Self-embeddings use matrix factorization techniques to convert category features into factor-based generalized embeddings. And in global embedding, document fuzzification is performed using Minhash and Latent Dirichlet Allocation models to transform uncertain and non-standard text documents into vectors that conform to the same probability distribution. In addition, we customize an appropriative neural network for each API semantic cluster to introduce asymmetries in complementarity recommendations and reconstruct complementary relations. Finally, the proposed approach outperforms baseline approaches through experiments using real-world datasets in both normal and long-tail cases." @default.
- W4385300518 created "2023-07-28" @default.
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- W4385300518 date "2022-12-01" @default.
- W4385300518 modified "2023-09-26" @default.
- W4385300518 title "Seizing the Long Tail: Neural Complementary Recommendation for Cloud API Delivery" @default.
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- W4385300518 doi "https://doi.org/10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00283" @default.
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