Using Implicit Preference Relations to Improve Recommender Systems

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Using Implicit Preference Relations to Improve Recommender Systems

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Using Implicit Preference Relations to Improve Recommender Systems

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

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

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal on Data Semantics

ISSN

1861-2032

e-ISSN

—

Svazek periodika

6

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

16

Strana od-do

15-30

Kód UT WoS článku

000398896700003

EID výsledku v databázi Scopus

2-s2.0-85013433868