# TPMCF: Temporal QoS Prediction using Multi-Source Collaborative Features

arXiv: https://arxiv.org/abs/2303.18201 · HTML: https://mlsanity.com/paper/2303.18201/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 60 references that are in the map.
- First posted on arXiv 30 Mar 2023.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Dive into Deep Learning](https://mlsanity.com/paper/2106.11342/) (2021)
- [Empirical Analysis of Predictive Algorithms for Collaborative Filtering](https://mlsanity.com/paper/1301.7363/) (2013)
- [Robust subspace clustering by Cauchy loss function](https://mlsanity.com/paper/1904.12274/) (2019)
- [CAHPHF: Context-Aware Hierarchical QoS Prediction with Hybrid Filtering](https://mlsanity.com/paper/2001.09897/) (2020)
- [CARP: Context-Aware Reliability Prediction of Black-Box Web Services](https://mlsanity.com/paper/1503.00102/) (2015)

## Built on by (most cited first, 3 of 3)

- [Using Causality for Enhanced Prediction of Web Traffic Time Series](https://mlsanity.com/paper/2502.00612/) (2025)
- [Modelling Concurrent RTP Flows for End-to-end Predictions of QoS in Real Time Communications](https://mlsanity.com/paper/2410.15846/) (2024)
- [Anomaly Resilient Temporal QoS Prediction using Hypergraph Convoluted Transformer Network](https://mlsanity.com/paper/2410.17762/) (2024)

## Abstract (arXiv)

Recently, with the rapid deployment of service APIs, personalized service recommendations have played a paramount role in the growth of the e-commerce industry. Quality-of-Service (QoS) parameters determining the service performance, often used for recommendation, fluctuate over time. Thus, the QoS prediction is essential to identify a suitable service among functionally equivalent services over time. The contemporary temporal QoS prediction methods hardly achieved the desired accuracy due to various limitations, such as the inability to handle data sparsity and outliers and capture higher-order temporal relationships among user-service interactions. Even though some recent recurrent neural-network-based architectures can model temporal relationships among QoS data, prediction accuracy degrades due to the absence of other features (e.g., collaborative features) to comprehend the relationship among the user-service interactions. This paper addresses the above challenges and proposes a scalable strategy for Temporal QoS Prediction using Multi-source Collaborative-Features (TPMCF), achieving high prediction accuracy and faster responsiveness. TPMCF combines the collaborative-features of users/services by exploiting user-service relationship with the spatio-temporal auto-extracted features by employing graph convolution and transformer encoder with multi-head self-attention. We validated our proposed method on WS-DREAM-2 datasets. Extensive experiments showed TPMCF outperformed major state-of-the-art approaches regarding prediction accuracy while ensuring high scalability and reasonably faster responsiveness.