Personalized Re-ranking for Improving Diversity in Live Recommender Systems

04/14/2020
by   Yichao Wang, et al.
0

Users of industrial recommender systems are normally suggesteda list of items at one time. Ideally, such list-wise recommendationshould provide diverse and relevant options to the users. However, in practice, list-wise recommendation is implemented as top-N recommendation. Top-N recommendation selects the first N items from candidates to display. The list is generated by a ranking function, which is learned from labeled data to optimize accuracy.However, top-N recommendation may lead to suboptimal, as it focuses on accuracy of each individual item independently and overlooks mutual influence between items. Therefore, we propose a personalized re-ranking model for improving diversity of the recommendation list in real recommender systems. The proposed re-ranking model can be easily deployed as a follow-up component after any existing ranking function. The re-ranking model improves the diversity by employing personalized Determinental Point Process (DPP). DPP has been applied in some recommender systems to improve the diversity and increase the user engagement.However, DPP does not take into account the fact that users may have individual propensities to the diversity. To overcome such limitation, our re-ranking model proposes a personalized DPP to model the trade-off between accuracy and diversity for each individual user. We implement and deploy the personalized DPP model on alarge scale industrial recommender system. Experimental results on both offline and online demonstrate the efficiency of our proposed re-ranking model.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/15/2019

Personalized Context-aware Re-ranking for E-commerce Recommender Systems

Ranking is a core task in E-commerce recommender systems, which aims at ...
research
02/24/2023

Slate-Aware Ranking for Recommendation

We see widespread adoption of slate recommender systems, where an ordere...
research
09/15/2017

Improving the Diversity of Top-N Recommendation via Determinantal Point Process

Recommender systems take the key responsibility to help users discover i...
research
06/29/2019

One Size Does Not Fit All: Modeling Users' Personal Curiosity in Recommender Systems

Today's recommender systems are criticized for recommending items that a...
research
05/18/2023

Improving Recommendation System Serendipity Through Lexicase Selection

Recommender systems influence almost every aspect of our digital lives. ...
research
04/17/2023

CAViaR: Context Aware Video Recommendations

Many recommendation systems rely on point-wise models, which score items...
research
11/13/2014

DUM: Diversity-Weighted Utility Maximization for Recommendations

The need for diversification of recommendation lists manifests in a numb...

Please sign up or login with your details

Forgot password? Click here to reset