|
Title
|
|
|
|
Collaborative filtering for binary, positiveonly data
| |
|
Author
|
|
|
|
| |
|
Abstract
|
|
|
|
| Traditional collaborative ltering assumes the availability of explicit ratings of users for items. However, in many cases these ratings are not available and only binary, positive-only data is available. Binary, positive-only data is typically associated with implicit feedback such as items bought, videos watched, ads clicked on, etc. However, it can also be the results of explicit feedback such as likes on social networking sites. Because binary, positive-only data contains no negative information, it needs to be treated differently than rating data. As a result of the growing relevance of this problem setting, the number of publications in this field increases rapidly. In this survey, we provide an overview of the existing work from an innovative perspective that allows us to emphasize surprising commonalities and key differences. |
| |
|
Language
|
|
|
|
English
| |
|
Source (journal)
|
|
|
|
SIGKDD explorations
| |
|
Publication
|
|
|
|
2017
| |
|
ISSN
|
|
|
|
1931-0145
1931-0153
| |
|
Volume/pages
|
|
|
|
19
:1
(2017)
, p. 1-21
| |
|
Full text (Publisher's DOI)
|
|
|
|
| |
|
Full text (publisher's version - intranet only)
|
|
|
|
| |
|