Data Science Chair

    Our paper "A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models" has been accepted at RecSys 2021


    Our case study on sampling strategies for evaluating neural sequential item recommendation models has been accepted for publication at RecSys 2021.

    Our paper "A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models" has been accepted for publication in the reproducibility track of the conference on Recommender Systems (RecSys 2021), which is the premier conference to present new research results, systems and techniques in the broad field of recommender systems. In this paper we analyse the current evaluation methods to rank neural sequential item recommendation models. In summary we find that these evaluation methods do not produce the same rankings and a re-evaluation is necessary for current state-of-the-art methods. 

    As a teaser here is the current abstract for the publication, we will later share the full text of the final publication:

    At the present time, sequential item recommendation models are compared by calculating metrics on a small item subset (target set) that is sampled from the full item set to speed up computation. The target set contains the relevant item and a set of negative items that are sampled from the full item set. Two well-known strategies to sample negative items are uniform random sampling and sampling by popularity to better approximate the item frequency distribution in the dataset. Most recently published papers on sequential item recommendation rely on sampling by popularity to compare the evaluated models. However, recent work has already shown that an evaluation with uniform random sampling may not be consistent with the exact ranking (i.e., the model ranking obtained by evaluating a metric using the full item set as target set), which raises the question whether the ranking obtained by sampling by popularity is equal to the exact ranking. In this work, we re-evaluate current state-of-the-art sequential recommender models from the point of view, whether these sampling strategies have an impact on the final ranking of the models. We therefore train four recently proposed sequential recommendation models on five widely known datasets. For each dataset and model, we employ three evaluation strategies. First, we compute the exact model ranking. Then we evaluate all models on a target set sampled by the two different sampling strategies, uniform random sampling and sampling by popularity with the commonly used target set size of 100, compute the model ranking for each strategy and compare them with each other. Additionally, we vary the size of the sampled target set. Overall, we find that both sampling strategies can produce inconsistent rankings compared with the true ranking of the models. Furthermore, both sampling by popularity and uniform random sampling do not consistently produce the same ranking when compared over different sample sizes. Our results suggest that like uniform random sampling, rankings obtained by sampling by popularity do not equal the exact ranking of recommender models and therefore both should be avoided in favor of the exact ranking when establishing state-of-the-art.