The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models
Abstract
Automatic Web-service selection is an important research topic in the domain of service computing. During this process, reliable predictions for quality of service (QoS) based on historical service invocations are vital to users. This work aims at making highly accurate predictions for missing QoS data via building an ensemble of nonnegative latent factor (NLF) models. Its motivations are: 1) the fulfillment of nonnegativity constraints can better represent the positive value nature of QoS data, thereby boosting the prediction accuracy and 2) since QoS prediction is a learning task, it is promising to further improve the prediction accuracy with a carefully designed ensemble model. To achieve this, we first implement an NLF model for QoS prediction. This model is then diversified through feature sampling and randomness injection to form a diversified NLF model, based on which an ensemble is built. Comparison results between the proposed ensemble and several widely employed and state-of-the-art QoS predictors on two large, real data sets demonstrate that the former can outperform the latter well in terms of prediction accuracy.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Learning the parts of objects by non-negative matrix factorization
Lee, D. D., & Seung, H. S. (1999) · Nature · doi:10.1038/44565 · Cited by 13,868
Matrix Factorization Techniques for Recommender Systems
Koren, Y., Bell, R., & Volinsky, C. (2009) · Computer · doi:10.1109/mc.2009.263 · Cited by 11,673
Adomavicius, G., & Tuzhilin, A. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.99 · Cited by 10,310
Ensemble methods in machine learning
Dietterich, T. G. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-45014-9_1 · Cited by 8,408
ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION
Lee, D. D., & Seung, H. S. (2000) · Cited by 7,952
Paatero, P., & Tapper, U. (1994) · Environmetrics · doi:10.1002/env.3170050203 · Cited by 6,023
Evaluating collaborative filtering recommender systems
Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004) · ACM Transactions on Information Systems · doi:10.1145/963770.963772 · Cited by 5,798
ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION
Seung, D., & Lee, L.-W. (2001) · Cited by 4,753
A Survey of Collaborative Filtering Techniques
Su, X., & Khoshgoftaar, T. M. (2009) · Advances in Artificial Intelligence · doi:10.1155/2009/421425 · Cited by 3,602
Cited by
Gao, S., Zhou, M., Wang, Y., Cheng, J., Yachi, H., & Wang, J. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2846646 · Cited by 698
Self-citationPosition-Transitional Particle Swarm Optimization-Incorporated Latent Factor Analysis
Luo, X., Yuan, Y., Chen, S., Zeng, N., & Wang, Z. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3033324 · Cited by 264
Self-citation
Metadata
- Authors
- Luo, X., Zhou, M., Xia, Y., Zhu, Q., Ammari, A. C., & Alabdulwahab, A.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2016-03
- Volume
- 27
- Issue
- 3
- Pages
- 524-537
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2056398894
- Cited by
- 260
- References
- 43
- FWCI
- 57.66
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models. https://app.cochat.ai/papers/10.1109/tnnls.2015.2412037
- The paper
- Luo, X., Zhou, M., Xia, Y., Zhu, Q., Ammari, A. C., & Alabdulwahab, A. (2016). Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2015.2412037
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
