an evaluation of simrank and personalized pagerank to build a recommender system for the web of data

WWW 2015 – 7th International Workshop on Web Intelligence & CommunitiesMay 18, 2015 in Florence, Italy

An evaluation of SimRank and Personalized PageRank tobuild a recommender system for the Web of Data

Phuong T. Nguyen, Paolo Tomeo, Tommaso Di Noia, Eugenio Di Sciascio

{phuong.nguyen, paolo.tomeo, tommaso.dinoia, eugenio.disciascio}@poliba.it

Polytechnic University of Bari - Bari (ITALY)


Outline

Introduction Recommender systems Linked Open Data SimRank and Personalized PageRank Experimental Evaluation Conclusions


Introduction

Content-based recommender systems base on the notion of similarity between items, but obviously they need content

Web of data is an opportunity to foster data-intensive applications

We have investigated how effectively SimRank and Personalized PageRank work with Linked Data in the recommendation task.


Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user.[F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors. Recommender Systems Handbook. Springer, 2011.]

Recommender Systems


Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user.[F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors. Recommender Systems Handbook. Springer, 2011.]

Recommender Systems

• Content-based filtering• Collaborative filtering


Content-based RSs try to recommend items similar to those a given user has liked in the past. The recommendations are based upon a description of the items and a profile of the user’s interests

Recommender System

User profile

Content-based RSs

Items description

Personalized Recommendations


We need domain knowledgeand rich descriptions of the items

No suitable suggestion without enough information

Quality of CB recommendations depends on quantity and quality of the features explicitly associated to the items

Main drawback: Limited Content Analysis

P. Lops, M. de Gemmis, G. Semeraro. Content-based Recommender Systems: State of the Art and Trends. In Recommender Systems Handbook: A Complete Guide for Research Scientists & Practitioners, Chapter 3, Springer, 2010.


Linked Open Data

• Initiative for publishing and connecting data on the Web using Semantic Web technologies

• >30 billion of RDF triples from hundreds of data sources• Good resource to mine existing information and deduce new

knowledge


Florence in DBpedia

Florence

Formers capital of Italydcterms:subject

Tuscany

dbpedia-owl:region

Italydbpedia-owl:country

.

.

.

Example of facts in Triple-form: subject- predicate - object


Enrich Data model

Catalog Items Knowledge Graph

1 – Mapping (Entity linking)2 – Subgraph extraction


Enrich Data model

Catalog Items Knowledge Graph

Each item is represented by a part of this subgraph.We need for algorithms able to compute similarity between graphs.


SimRank

SimRank computes similarity between nodes in a graph using the structural context: two nodes are similar if they are referenced by similar nodes.

G. Jeh and J. Widom. Simrank: A measure of structural-context similarity. In ACM KDD ’02, pages 538–543, 2002.

Given k ≥ 0, R(k) (α, β) = 1 with α = β. R(k) (α, β) = 0 with k = 0 and α = β. Otherwise, the general formula is


Personalized PageRank

A node receives an amount of rank from every node which points to it and in turn transfers an amount of its rank to the nodes it refers to.

T. H. Haveliwala. Topic-sensitive pagerank. In ACM WWW ’02, pages 517–526, 2002. ACM.

The similarity between two items α and β represented by vectors α = {ai }i=1,..,n

and β = {bi }i=1,..,n is computed as the inner product space between the two vectors


K-Nearest Neighbors

K-Nearest Neighbors (k-NN) algorithm find the set neighbors(α) containing the k most similar entities β to a given item α using a similarity function sim(α, β)

A k-NN recommendation algorithm predicts the rating for item α for an user u, taking into account the neighbors of α and the profile of u


Evaluation Methodology

• Top-N Item recommendation task, for different number of N (from top-1 to top-50 )

• 60-40% holdout split for each user

• Baselines: Vector Space Model (VSM) and a simplified variation of the algorithm proposed in [T. Di Noia, R. Mirizzi, V. C. Ostuni, D. Romito, and M. Zanker. Linked open data to support content-based recommender systems. In ACM I-SEMANTICS ’12. ACM]

• The results for the four algorithms were computed with 10, 20, 40, 60, 80 neighbors


Evaluation Metrics

Accuracy Precision (P@N) Recall (R@N)

Sales diversity Catalog coverage Entropy Gini index

Novelty Long-tail percentage Expected Popularity Complement (EPC@N)


Dataset

Subset of Last.fm mapped to DBpedia• 1,867 users• 700 most popular artists and bands• 47,330 ratings• 113,386 number of extracted triples

Mappingshttp://sisinflab.poliba.it/semanticweb/lod/recsys/datasets/


Properties extracted

Properties extracted from DBpedia (113,386 extracted triples)

Inbound Outbounddbo:producerdbo:artistdbo:writerdbo:associatedBanddbo:associatedMusicalArtistdbo:musicalArtistdbo:musicComposerdbo:bandMemberdbo:formerBandMemberdbo:starringdbo:composer

dcterms:subjectdbo:genredbo:associatedBanddbo:associatedMusicalArtistdbo:instrumentdbo:occupationdbo:birthPlacedbo:background


Accuracy with 40 neighbors

PageRank and SimRank more accurate


Catalog coverage with 40 neighbors

Worst coverage


Distribution with 40 neighbors

Recommendations concentred on a few items


Novelty Results


Conclusions

SimRank and Personalized PageRank obtained interesting results compared to the two baselines in terms of precision, recall and novelty, even though catalog coverage and items distribution decrease

Future work:Same experiments on two more dataset: Movielens and

TheLibraryThingSame experiments using other graph similarity metrics


Thanks for your attention!

Q & A

an evaluation of simrank and personalized pagerank to build a recommender system for the web of data

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