Price comparison services are widely used by e-shopping customers. Such e-shopping sites receive product offers from thousands of online stores, and in order to provide price comparison, product categorization, and searching, it is necessary to match different offers referring to the same real-world product. This is a hard task, since they need to classify millions of product offers in thousands of classes, and distinct descriptions may exist for the same product, as well as very similar descriptions of distinct products. In this work, we propose a method that uses association rules to classify product offers from e-shopping web sites matching offers against offers without the need for a product catalog. This is a supervised learning method that trains a classifier, whose generated model comprises a set of association rules to identify product offer classes. Experimental evaluations show that our method is effective and efficient, and obtains better results than three baselines in several datasets with distinct characteristics. It is able to deal with large datasets containing thousands of classes and different types of products such as electronics and books. Moreover, we propose and evaluate strategies to reduce its execution time and we evaluate its weaknesses.
AanenS.S.VandicD. and FrasincarF., Automated product taxonomy mapping in an e-commerce environment, Expert Systems with Applications42(3) (2015), 1298–1313.
2.
AgrawalR. and IeongS., Aggregating web offers to determine product prices, in: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’12, ACM, New York, NY, USA (2012), 435–443.
3.
AgrawalR. and SrikantR., Fast algorithms for mining association rules, in: Proceedings of the 20th International Conference on Very Large Data Bases, Santiago, Chile (1994), 487–499.
4.
Baeza-YatesR. and Ribeiro-NetoB., Modern Information Retrieval: The Concepts and Technology Behind Search, Addison-Wesley Professional, 2011.
5.
BenjellounO.Garcia-MolinaH.MenestrinaD.SuQ.WhangS.E. and WidomJ., Swoosh: A generic approach to entity resolution, The VLDB Journal18(1) (January 2009), 255–276.
6.
BreimanL., Random forests, Machine Learning45(1) (October 2001), 5–32.
7.
ChenL. and WangS., Automated feature weighting in naive bayes for high-dimensional data classification, in: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM’12, ACM, New York, NY, USA (2012), 1243–1252.
8.
CortezE.HerreraM.R.da SilvaA.S.de MouraE.S. and NeubertM., Lightweight methods for large-scale product categorization, Journal of the American Society for Information Science and Technology62(9) (September 2011), 1839–1848.
9.
CrammerK. and SingerY., On the algorithmic implementation of multiclass kernel-based vector machines, The Journal of Machine Learning Research2 (2002), 265–292.
10.
ElmagarmidA.K.IpeirotisP.G. and VerykiosV.S., Duplicate record detection: A survey, IEEE Transaction on Knowledge and Data Engineering19(1) (2007), 1–16.
11.
FrenchJ.C.PowellA.L. and SchulmanE., Using clustering strategies for creating authority files, Journal of the American Society for Information Science51(8) (2000), 774–786.
12.
GetoorL. and MachanavajjhalaA., Entity resolution: Theory, practice and open challenges, Proceedings of the VLDB Endowment5(12) (2012), 2018–2019. Tutorial available at http://www.cs.umd.edu/∼getoor/Tutorials/ER_VLDB2012.pdf.
13.
GopalakrishnanV.IyengarS.MadaanA.RastogiR. and SengameduS., Matching product titles using web-based enrichment, in: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM ’12, ACM, New York, NY, USA (2012), 605–614.
14.
IoannouE.RassadkoN. and VelegrakisY., On Generating Benchmark Data for Entity Matching, Journal on Data Semantics2(1) (2013), 37–56.
15.
JaccardP., Étude comparative de la distribuition florale dans une portion des alpes et des jura, Bulletin Del La Société Vaudoise Des Sciences Naturelles37 (1901), 547–579.
16.
KannanA.GivoniI.E.AgrawalR. and FuxmanA., Matching unstructured product offers to structured product specifications, in: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, New York, NY, USA, San Diego, USA (2011), 404–412.
17.
KöpckeH. and RahmE., Frameworks for entity matching: A comparison, Data & Knowledge Engineering69(2) (2010), 197–210.
18.
KöpckeH.ThorA. and RahmE., Evaluation of entity resolution approaches on real-world match problems, Proceedings of the VLDB Endowment3(1–2) (2010), 484–493.
19.
KöpckeH.ThorA.ThomasS. and RahmE., Tailoring entity resolution for matching product offers, in: Proceedings of the 15th International Conference on Extending Database Technology, ACM, New York, NY, USA, Berlin, Germany (2012), 545–550.
20.
KoudasN.SarawagiS. and SrivastavaD., Record linkage: Similarity measures and algorithms, in: Proceedings of the ACM SIGMOD International Conference on Management of Data, ACM New York, NY, USA, Chicago, USA (June 2006), 802–803.
21.
LondheN.GopalakrishnanV.ZhangA.NgoH.Q. and SrihariR., Matching titles with cross title web-search enrichment and community detection, Proceedings of the VLDB Endowment7(12) (Aug 2014), 1167–1178.
22.
NguyenH.FuxmanA.PaparizosS.FreireJ. and AgrawalR., Synthesizing products for online catalogs, Proceedings of the VLDB Endowment4(7) (April 2011), 409–418.
23.
PavlovD.BalasubramanyanR.DomB.KapurS. and ParikhJ., Document preprocessing for naive bayes classification and clustering with mixture of multinomials, in: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, New York, NY, USA, Seattle, USA (2004), 829–834.
24.
PereiraD.A.da SilvaE.E.B. and EsminA.A.A., Disambiguating publication venue titles using association rules, in: Proceedings of the IEEE/ACM Joint Conference on Digital Libraries, London, UK (Setember 2014), 77–86.
25.
PereiraD.A.Ribeiro-NetoB.ZivianiN.LaenderA.H.F. and GonçalvesM.A., A generic web-based entity resolution framework, Journal of the American Society for Information Science and Technology62(5) (May 2011), 919–932.
26.
SaltonG. and McGillM.J., Introduction to Modern Information Retrieval, McGraw-Hill, Inc., New York, NY, USA, 1983.
27.
VapnikV.N., The Nature of Statistical Learning Theory, Springer-Velag, New York, USA, 1995.
28.
VelosoA.FerreiraA.A.GonçalvesM.A.LaenderA.H.F. and Jr.W.M., Cost-effective on-demand associative author name disambiguation, Information Processing and Management48(4) (2012), 680–697.
29.
VelosoA.Jr.W.M.CristoM.GonçalvesM.A. and ZakiM.J., Multi-evidence, multi-criteria, lazy associative document classification, in: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, ACM, New York, NY, USA, Arlington, Virginia, USA (2006), 218–227.
30.
WedyanS., Review and comparison of associative classification data mining approaches, International Journal of Computer, Electrical, Automation, Control and Information Engineering8(1) (2014), 34–45.
31.
WittenI.H.FrankE. and HallM.A., Data Mining: Practical Machine Learning Tools and Techniques, third edition, Morgan Kaufmann, 2011.
32.
WolinB., Automatic classification in product catalogs, in: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, New York, NY, USA (2002), 351–352.
33.
XuB.HuangJ.Z.WilliamsG.WangQ. and YeY., Classifying very high-dimensional data with random forests built from small subspaces, International Journal of Data Warehousing and Mining8(2) (2012), 44–63.
34.
ZhangY.MukherjeeR. and SoetarmanB., Concept extraction and e-commerce applications, Electronic Commerce Research and Applications12(4) (2013), 289–296. Social Commerce-Part 2.
