Using Implicit Preference Relations to Improve Recommender Systems

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F17%3A10320134" target="_blank" >RIV/00216208:11320/17:10320134 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/article/10.1007/s13740-016-0061-8" target="_blank" >http://link.springer.com/article/10.1007/s13740-016-0061-8</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s13740-016-0061-8" target="_blank" >10.1007/s13740-016-0061-8</a>

Result language
angličtina
Original language name
Using Implicit Preference Relations to Improve Recommender Systems
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
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Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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