Journal on Data Semantics

, Volume 6, Issue 1, pp 15–30 | Cite as

Using Implicit Preference Relations to Improve Recommender Systems

  • Ladislav Peska
  • Peter Vojtas
Original Article


Our work is generally focused on making recommendations for small or medium-sized e-commerce portals, where we are facing scarcity of explicit feedback, low user loyalty, short visit durations or a low number of visited objects. In this paper, we present a novel approach to use a specific user behavior pattern as implicit feedback, forming binary relations between objects. Our hypothesis is that if a user selects a specific object from the list of displayed objects, it is an expression of his/her binary preference between the selected object and others that are visible, but ignored. We expand this relation with content-based similarity of objects. We define implicit preference relation (IPR) a partial ordering of objects based on similarity expansion of ignored-selected preference relation. We propose a merging algorithm utilizing the synergic effect of two approaches this IPR partial ordering and a list of recommended objects based on any/another algorithm. We report on a series of offline experiments with various recommending algorithms on two real-world e-commerce datasets. The merging algorithm could improve the ranked list of most of the evaluated algorithms in terms of nDCG. Furthermore, we also provide access to the relevant datasets and source codes for further research.


Recommender systems Implicit preference relations  User preference Implicit feedback  E-Commerce Repeatable experiments 



This work was supported by Czech Grants No. SVV-2015-260222, P46 and GAUK-126313. Some additional materials were made available online: – Secondhand Bookshop dataset: –Travel Agency dataset: – IPIget component: – IPR source codes:


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Faculty of Mathematics and PhysicsCharles University in PraguePragueCzech Republic

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