CLEF 2017 NewsREEL Overview: Offline and Online Evaluation of Stream-based News Recommender Systems

The CLEF NewsREEL challenge allows researchers to evaluate news recommendation algorithms both online (NewsREEL Live) and offline (News- REEL Replay). Compared with the previous year NewsREEL challenged participants with a higher volume of messages and new news portals. In the 2017 edition of the CLEF NewsREEL challenge a wide variety of new approaches have been implemented ranging from the use of existing machine learning frameworks, to ensemble methods to the use of deep neural networks. This paper gives an overview over the implemented approaches and discusses the evaluation results. In addition, the main results of Living Lab and the Replay task are explained

CLEF NewsREEL 2016: Comparing Multi-Dimensional Offline and Online Evaluation of News Recommender Systems

Running in its third year at CLEF, NewsREEL challenged participants to develop news recommendation algorithms and have them benchmarked in an online (Task 1) and offline setting (Task 2), respectively. This paper provides an overview of the NewsREEL scenario, outlines this year’s campaign, presents results of both tasks, and discusses the approaches of participating teams. Moreover, it overviews ideas on living lab evaluation that have been presented as part of a “New Ideas” track at the conference in Portugal. Presented results illustrate potentials for multi-dimensional evaluation of recommendation algorithms in a living lab and simulation based evaluation setting

Evaluation of recommender systems in streaming environments

Evaluation of recommender systems is typically done with finite datasets. This means that conventional evaluation methodologies are only applicable in offline experiments, where data and models are stationary. However, in real world systems, user feedback is continuously generated, at unpredictable rates. Given this setting, one important issue is how to evaluate algorithms in such a streaming data environment. In this paper we propose a prequential evaluation protocol for recommender systems, suitable for streaming data environments, but also applicable in stationary settings. Using this protocol we are able to monitor the evolution of algorithms' accuracy over time. Furthermore, we are able to perform reliable comparative assessments of algorithms by computing significance tests over a sliding window. We argue that besides being suitable for streaming data, prequential evaluation allows the detection of phenomena that would otherwise remain unnoticed in the evaluation of both offline and online recommender systems.Comment: Workshop on 'Recommender Systems Evaluation: Dimensions and Design' (REDD 2014), held in conjunction with RecSys 2014. October 10, 2014, Silicon Valley, United State

Reducing Offline Evaluation Bias in Recommendation Systems

Recommendation systems have been integrated into the majority of large online systems. They tailor those systems to individual users by filtering and ranking information according to user profiles. This adaptation process influences the way users interact with the system and, as a consequence, increases the difficulty of evaluating a recommendation algorithm with historical data (via offline evaluation). This paper analyses this evaluation bias and proposes a simple item weighting solution that reduces its impact. The efficiency of the proposed solution is evaluated on real world data extracted from Viadeo professional social network.Comment: 23rd annual Belgian-Dutch Conference on Machine Learning (Benelearn 2014), Bruxelles : Belgium (2014

Whole-Chain Recommendations

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems. In practical recommendation sessions, users will sequentially access multiple scenarios, such as the entrance pages and the item detail pages, and each scenario has its specific characteristics. However, the majority of existing RL-based recommender systems focus on optimizing one strategy for all scenarios or separately optimizing each strategy, which could lead to sub-optimal overall performance. In this paper, we study the recommendation problem with multiple (consecutive) scenarios, i.e., whole-chain recommendations. We propose a multi-agent RL-based approach (DeepChain), which can capture the sequential correlation among different scenarios and jointly optimize multiple recommendation strategies. To be specific, all recommender agents (RAs) share the same memory of users' historical behaviors, and they work collaboratively to maximize the overall reward of a session. Note that optimizing multiple recommendation strategies jointly faces two challenges in the existing model-free RL model - (i) it requires huge amounts of user behavior data, and (ii) the distribution of reward (users' feedback) are extremely unbalanced. In this paper, we introduce model-based RL techniques to reduce the training data requirement and execute more accurate strategy updates. The experimental results based on a real e-commerce platform demonstrate the effectiveness of the proposed framework.Comment: 29th ACM International Conference on Information and Knowledge Managemen