Using the Context of User Feedback in Recommender Systems

Ladislav Peska
(Charles University in Prague, Faculty of Mathematics and Physics)

Our work is generally focused on recommending for small or medium-sized e-commerce portals, where explicit feedback is absent and thus the usage of implicit feedback is necessary. Nonetheless, for some implicit feedback features, the presentation context may be of high importance. In this paper, we present a model of relevant contextual features affecting user feedback, propose methods leveraging those features, publish a dataset of real e-commerce users containing multiple user feedback indicators as well as its context and finally present results of purchase prediction and recommendation experiments. Off-line experiments with real users of a Czech travel agency website corroborated the importance of leveraging presentation context in both purchase prediction and recommendation tasks.

In Jan Bouda, Lukáš Holík, Jan Kofroň, Jan Strejček and Adam Rambousek: Proceedings 11th Doctoral Workshop on Mathematical and Engineering Methods in Computer Science (MEMICS 2016), Telč, Czech Republic, 21st-23rd October 2016, Electronic Proceedings in Theoretical Computer Science 233, pp. 1–12.
Published: 13th December 2016.

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