Trinity: Walking on a User-Object-Tag Heterogeneous Network for Personalised Recommendations
| Autor: | Rui Jiang, Lily Sun, Mingxin Gan |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Information retrieval
business.industry Computer science 02 engineering and technology Recommender system Object (computer science) computer.software_genre MovieLens Computer Science Applications Theoretical Computer Science Upload Resource (project management) Computational Theory and Mathematics Hardware and Architecture 020204 information systems 0202 electrical engineering electronic engineering information engineering Collaborative filtering 020201 artificial intelligence & image processing The Internet Data mining business computer Software Heterogeneous network |
| Zdroj: | Journal of Computer Science and Technology. 31:577-594 |
| ISSN: | 1860-4749 1000-9000 |
| Popis: | The rapid evolution of the Internet has been appealing for effective recommender systems to pinpoint useful information from online resources. Although historical rating data has been widely used as the most important information in recommendation methods, recent advancements have been demonstrating the improvement in recommendation performance with the incorporation of tag information. Furthermore, the availability of tag annotations has been well addressed by such fruitful online social tagging applications as CiteULike, MovieLens and BibSonomy, which allow users to express their preferences, upload resources and assign their own tags. Nevertheless, most existing tag-aware recommendation approaches model relationships among users, objects and tags using a tripartite graph, and hence overlook relationships within the same types of nodes. To overcome this limitation, we propose a novel approach, Trinity, to integrate historical data and tag information towards personalised recommendation. Trinity constructs a three-layered object-user-tag network that considers not only interconnections between different types of nodes but also relationships within the same types of nodes. Based on this heterogeneous network, Trinity adopts a random walk with restart model to assign the strength of associations to candidate objects, thereby providing a means of prioritizing the objects for a query user. We validate our approach via a series of large-scale 10-fold cross-validation experiments and evaluate its performance using three comprehensive criteria. Results show that our method outperforms several existing methods, including supervised random walk with restart, simulation of resource allocating processes, and traditional collaborative filtering. |
| Databáze: | OpenAIRE |
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