Modelling Concept Drift in Dynamic Data Streams for Recommender Systems

ACM Transactions on Recommender Systems, vol. 3, pp. 1–28

Abstract

Recommendation systems play a crucial role in modern e-commerce and streaming services. However, the limited availability of public datasets hampers the rapid development of more efficient and accurate recommendation algorithms within the research community. This work introduces a stream-based data generator designed to generate user preferences for a set of items while accommodating progressive changes in user preferences. The underlying principle involves using user/item embeddings to derive preferences by exploring the proximity of these embeddings. Whether randomly generated or learned from a real finite data stream, these embeddings serve as the basis for generating new preferences. We investigate how this fundamental model can adapt to shifts in user behavior over time; in our framework, changes correspond to alterations in the structure of the tripartite graph, reflecting modifications in the underlying embeddings. Through an analysis of real-life data streams, we demonstrate that the proposed model is effective in capturing actual preferences and the changes that they can exhibit over time. Thus, we characterize these changes and develop a generalized method capable of simulating realistic data, thereby generating streams with similar yet controllable drift dynamics.

Authors 6

1. Luciano Caroprese

   Institute for High Performance Computing and Networking · University of Chieti-Pescara

   Affiliation as printed

   University Gabriele d'Annunzio of Chieti and Pescara, Department of Engineering and Geosciences, Pescara, Italy and ICAR-CNR, Rende, Italy

   ICAR-CNR, Rende, Italy

   University Gabriele d'Annunzio of Chieti and Pescara Department of Engineering and Geosciences, Pescara, Italy

2. Francesco Sergio Pisani

   Institute for High Performance Computing and Networking

   Affiliation as printed

   ICAR-CNR, Rende, Italy

3. Bruno Veloso

   Universidade Portucalense · INESC TEC

   Affiliation as printed

   Universidade Portucalense, Porto, Portugal and INESC TEC, Porto, Portugal

   INESC TEC, Porto, Portugal

   Universidade Portucalense, Porto, Portugal

4. Giuseppe Manco

   Institute for High Performance Computing and Networking

   Affiliation as printed

   ICAR-CNR, Rende, Italy

5. Holger H. Hoos Aachen

   Leiden University · University of British Columbia

   Affiliation as printed

   Leiden University, Leiden Netherlands and The University of British Columbia, Vancouver, Canada

   Leiden University, Leiden Netherlands

   The University of British Columbia, Vancouver, Canada

6. João Manuel Portela da Gama

   Universidade Portucalense · INESC TEC

   Affiliation as printed

   Universidade Portucalense, Porto, Portugal and INESC TEC, Porto, Portugal

   INESC TEC, Porto, Portugal

   Universidade Portucalense, Porto, Portugal