An Ensemble Approach for News Recommendation Based on Contextual Bandit Algorithms

An Ensemble Approach for News Recommendation Based on Contextual Bandit Algorithms

Liang, Yu (TU Delft Electrical Engineering, Mathematics and Computer Science)

Larson, Martha (mentor)

Delft University of Technology

2017-09-29

News recommendation is a field different from traditional recommendation fields. News articles are created and deleted continuously with a very short life cycle. Users' preference is also hard to model since they can easily be attracted by things happening around them. With all those challenges, traditional recommendation approaches, such as content-based filtering and collaborative filtering, do not work well in the field. Simple recency-based or popularity-based recommenders do work well. However, even the recommender with the highest performance has its restriction. In this work, we build an ensemble model to combine the power of different recommenders. We build up a delegation model on top of several news recommenders based on various contextual bandit algorithms (a combination of multi-armed bandit algorithms and context information). The delegation model is responsible for delegating recommendation requests to the appropriate recommender with the purpose to maximize the Click Through Rate (CTR) from the recommendations and can update continuously with users' feedback. We evaluate the performance of our delegation-model-based recommender in both online and offline scenarios with the evaluation methods provided by CLEF-NEWSREEL Challenge 2017. Furthermore, we also evaluate the response time of our delegation model to see whether it is feasible to run online. The results show that our proposed delegation model can choose the appropriate recommender to serve the incoming requests each time, improve its performance regarding CTR and is feasible to run in real-world settings. Additionally, we also evaluate our delegation-model-based recommender with another evaluation metric, catalog coverage. In our future work, we would like to combine more recommenders and explore more context features to further improve CTR.

News recommendation
Contextual bandit algorithms
Multi-armed bandit
Context
Recommender systems
Online evaluation
Offline evaluation
Ensemble recommender

http://resolver.tudelft.nl/uuid:45600ff4-3d22-4093-baf9-f8df40dcc211

Student theses

master thesis

© 2017 Yu Liang