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Online to offline (O2O) service recommendation method based on multi -dimensional similarity measurement
Stockholm University, Faculty of Social Sciences, Stockholm Business School. University of Chinese Academy of Sciences, China.
Number of Authors: 3
2017 (English)In: Decision Support Systems, ISSN 0167-9236, E-ISSN 1873-5797, Vol. 103, 1-8 p.Article in journal (Refereed) Published
Abstract [en]

With the rapid development of information technology, consumers are able to search for and buy services or products online, and then consume them in an offline store. This emerging ecommerce model is called online to offline (O2O) service, which has attracted business and academic attention. The large number of O2O services on the Internet creates a scalability problem, creating massive but highly sparse matrices relating customers to items purchased. In this paper, we proposed a novel O2O service recommendation method based on multidimensional similarity measurements. This approach encompasses three similarity measures: collaborative similarity, preference similarity and trajectory similarity. Experimental results show that a combination of multiple similarity measures performs better than any one single similarity measure. We also find that trajectory similarity performs better than the rating-based similarity metrics (collaborative similarity and preference similarity) in sparse matrices.

Place, publisher, year, edition, pages
2017. Vol. 103, 1-8 p.
Keyword [en]
O2O service, Recommendation system, Similarity measurement, Sparse matrix
National Category
Computer and Information Sciences Economics and Business
Identifiers
URN: urn:nbn:se:su:diva-150002DOI: 10.1016/j.dss.2017.08.003ISI: 000415772500001OAI: oai:DiVA.org:su-150002DiVA: diva2:1168593
Available from: 2017-12-21 Created: 2017-12-21 Last updated: 2018-01-13Bibliographically approved

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