From preference into decision making: Modeling user interactions in recommender systems

Qian Zhao, Martijn C. Willemsen, Gediminas Adomavicius, F. Maxwell Harper, Joseph A. Konstan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

User-system interaction in recommender systems involves three aspects: temporal browsing (viewing recommendation lists and/or searching/fltering), action (performing actions on recommended items, e.g., clicking, consuming) and inaction (neglecting or skipping recommended items). Modern recommenders build machine learning models from recordings of such user interaction with the system, and in doing so they commonly make certain assumptions (e.g., pairwise preference orders, independent or competitive probabilistic choices, etc.). In this paper, we set out to study the efects of these assumptions along three dimensions in eight diferent single models and three associated hybrid models on a user browsing data set collected from a real-world recommender system application. We further design a novel model based on recurrent neural networks and multi-task learning, inspired by Decision Field Theory, a model of human decision making. We report on precision, recall, and MAP, fnding that this new model outperforms the others.

Original languageEnglish (US)
Title of host publicationRecSys 2019 - 13th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Pages29-33
Number of pages5
ISBN (Electronic)9781450362436
DOIs
StatePublished - Sep 10 2019
Event13th ACM Conference on Recommender Systems, RecSys 2019 - Copenhagen, Denmark
Duration: Sep 16 2019Sep 20 2019

Publication series

NameRecSys 2019 - 13th ACM Conference on Recommender Systems

Conference

Conference13th ACM Conference on Recommender Systems, RecSys 2019
CountryDenmark
CityCopenhagen
Period9/16/199/20/19

Bibliographical note

Funding Information:
This work was supported by the National Science Foundation under grant IIS-1319382. The frst author was also supported by the Doctoral Dissertation Fellowship, 2016-17, by the Graduate School at the University of Minnesota.

Keywords

  • Decision feld theory
  • Decision making
  • Recurrent neural networks

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