An evaluation of SimRank and Personalized PageRank to build a recommender system for the Web of Data
7th International Workshop on Web Intelligence & Communities - -2015
Tomeo Paolo, Nguyen Phuong, Di Noia Tommaso
, Di Sciascio Eugenio
The Web of Data is the natural evolution of the World Wide Web from a set of interlinked documents to a set of interlinked entities. It is a graph of information resources interconnected by semantic relations, thereby yielding the name Linked Data. The proliferation of Linked Data is for sure an opportunity to create a new family of data-intensive applications such as recommender systems. In particular, since content-based recommender systems base on the notion of similarity between items, the selection of the right graph-based similarity metric is of paramount importance to build an effective recommendation engine. In this paper, we review two existing metrics, SimRank and PageRank, and investigate their suitability and performance for computing similarity between resources in RDF graphs and investigate their usage to feed a content-based recommender system. Finally, we conduct experimental evaluations on a dataset for musical artists and bands recommendations thus comparing our results with two other content-based baselines measuring their performance with precision and recall, catalog coverage, items distribution and novelty metrics.
Download: An evaluation of SimRank and Personalized PageRank to build a recommender system for the Web of Data - WIC @ WWW 2015.pdf
author = "Tomeo, Paolo and Nguyen, Phuong and Di Noia, Tommaso and Di Sciascio, Eugenio",
title = "An evaluation of SimRank and Personalized PageRank to build a recommender system for the Web of Data",
booktitle = "7th International Workshop on Web Intelligence \& Communities",
year = "2015",
publisher = "ACM",
url = "http://sisinflab.poliba.it/Publications/2015/TNDD15"