Ranking item features by mining online user-item interactions

Sofiane Abbar, Habibur Rahman, Saravanan Thirumuruganathan, Carlos Castillo, Gautam Das

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

Abstract

We assume a database of items in which each item is described by a set of attributes, some of which could be multi-valued. We refer to each of the distinct attribute values as a feature. We also assume that we have information about the interactions (such as visits or likes) between a set of users and those items. In our paper, we would like to rank the features of an item using user-item interactions. For instance, if the items are movies, features could be actors, directors or genres, and user-item interaction could be user liking the movie. These information could be used to identify the most important actors for each movie. While users are drawn to an item due to a subset of its features, a user-item interaction only provides an expression of user preference over the entire item, and not its component features. We design algorithms to rank the features of an item depending on whether interaction information is available at aggregated or individual level granularity and extend them to rank composite features (set of features). Our algorithms are based on constrained least squares, network flow and non-trivial adaptations to non-negative matrix factorization. We evaluate our algorithms using both real-world and synthetic datasets.

Original languageEnglish
Title of host publication2014 IEEE 30th International Conference on Data Engineering, ICDE 2014
PublisherIEEE Computer Society
Pages460-471
Number of pages12
ISBN (Print)9781479925544
DOIs
Publication statusPublished - 1 Jan 2014
Event30th IEEE International Conference on Data Engineering, ICDE 2014 - Chicago, IL, United States
Duration: 31 Mar 20144 Apr 2014

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627

Other

Other30th IEEE International Conference on Data Engineering, ICDE 2014
CountryUnited States
CityChicago, IL
Period31/3/144/4/14

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ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

Cite this

Abbar, S., Rahman, H., Thirumuruganathan, S., Castillo, C., & Das, G. (2014). Ranking item features by mining online user-item interactions. In 2014 IEEE 30th International Conference on Data Engineering, ICDE 2014 (pp. 460-471). [6816673] (Proceedings - International Conference on Data Engineering). IEEE Computer Society. https://doi.org/10.1109/ICDE.2014.6816673