Top-Personalized-K Recommendation

WONBIN KWEON, SeongKu Kang, Sanghwan Jang, Hwanjo Yu

Keywords: Recommender System, Collaborative Filtering, Personalization, Recommendation Size

TL;DR: We propose Top-Personalized-K Recommendation, a new recommendation task that provides a ranking list of the personalized size to maximize individual user satisfaction, along with PerK as a solution to the proposed task.

Abstract:

The conventional top-K recommendation, which presents the top-K items with the highest ranking scores, is a common practice for generating personalized ranking lists.
However, is this fixed-size top-K recommendation the optimal approach for every user’s satisfaction?
Not necessarily.
We point out that providing fixed-size recommendations without taking into account user utility can be suboptimal, as it may unavoidably include irrelevant items or limit the exposure to relevant ones.
To address this issue, we introduce Top-Personalized-K Recommendation, a new recommendation task aimed at generating a personalized-sized ranking list to maximize individual user satisfaction.
As a solution to the proposed task, we develop a model-agnostic framework named PerK.
PerK estimates the expected user utility by leveraging calibrated interaction probabilities, subsequently selecting the recommendation size that maximizes this expected utility.
Through extensive experiments on real-world datasets, we demonstrate the superiority of PerK in Top-Personalized-K recommendation task.
We expect that Top-Personalized-K recommendation has the potential to offer enhanced solutions for various real-world recommendation scenarios, based on its great compatibility with existing models.

Track: User Modeling and Recommendation

Submission Guidelines Scope: Yes

Submission Guidelines Blind: Yes

Submission Guidelines Format: Yes

Submission Guidelines Limit: Yes

Submission Guidelines Authorship: Yes

Student Author: Yes

Submission Number: 568