Trinity： Walking on a User-Object-Tag Heterogeneous Network for Personalised Recommendations  

作　　者：Ming-Xin Gan Lily Sun Rui Jiang 

机构地区：[1]Department of Management Science and Engineering, Donlinks School of Economics and Management University of Science and Technology Beijing, Beijing 100083, China [2]Department of Statistics, University of California Berkeley, Berkeley, CA 94720, U.S.A. [3]School of Systems Engineering, University of Reading, Reading, RG6 6UR, U.K. [4]Department of Automation, Tsinghua University, Beijing 100084, China

出　　处：《Journal of Computer Science & Technology》2016年第3期577-594,共18页计算机科学技术学报（英文版）

基　　金：This work was partially supported by the National Natural Science Foundation of China under Grant Nos. 71101010 and 71471016.

摘　　要：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.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.

关 键 词：recommender system tag-aware recommendation random walk with restart heterogeneous network socialtag 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论] TP311.13[自动化与计算机技术—计算机科学与技术]