Learning from sets of items in recommender systems

Mohit Sharma, F. Maxwell Harper, George Karypis

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Most of the existing recommender systems use the ratings provided by users on individual items. An additional source of preference information is to use the ratings that users provide on sets of items. The advantages of using preferences on sets are twofold. First, a rating provided on a set conveys some preference information about each of the set's items, which allows us to acquire a user's preferences for more items than the number of ratings that the user provided. Second, due to privacy concerns, users may not be willing to reveal their preferences on individual items explicitly but may be willing to provide a single rating to a set of items, since it provides some level of information hiding. This article investigates two questions related to using set-level ratings in recommender systems. First, how users' item-level ratings relate to their set-level ratings. Second, how collaborative filtering-based models for item-level rating prediction can take advantage of such set-level ratings. We have collected set-level ratings from active users of Movielens on sets of movies that they have rated in the past. Our analysis of these ratings shows that though the majority of the users provide the average of the ratings on a set's constituent items as the rating on the set, there exists a significant number of users that tend to consistently either under- or over-rate the sets. We have developed collaborative filtering-based methods to explicitly model these user behaviors that can be used to recommend items to users. Experiments on real data and on synthetic data that resembles the under- or over-rating behavior in the real data demonstrate that these models can recover the overall characteristics of the underlying data and predict the user's ratings on individual items.

Original languageEnglish (US)
Article number19
JournalACM Transactions on Interactive Intelligent Systems
Volume9
Issue number4
DOIs
StatePublished - Jul 2019

Bibliographical note

Funding Information:
This work was supported in part by NSF (1447788, 1704074, 1757916, 1834251), Army Research Office (W911NF1810344), Intel Corp, and the Digital Technology Center at the University of Minnesota. Access to research and computing facilities was provided by the Digital Technology Center and the Minnesota Supercomputing Institute.

Funding Information:
This work was supported in part by NSF (1447788, 1704074, 1757916, 1834251), Army Research Office (W911NF1810344), Intel Corp, and the Digital Technology Center at the University of Minnesota. Access to research and computing facilities was provided by the Digital Technology Center and the Minnesota Supercomputing Institute. Authors’ addresses: M. Sharma, F. M. Harper, and G. Karypis, University of Minnesota; emails: {sharm163, max, karypis}@ umn.edu. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2019 Association for Computing Machinery. 2160-6455/2019/07-ART19 $15.00 https://doi.org/10.1145/3326128

Keywords

  • Collaborative filtering
  • Matrix factorization
  • Recommender systems
  • User behavior modeling

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