A Re-visit of the Popularity Baseline in Recommender Systems

Popularity is often included in experimental evaluation to provide a reference performance for a recommendation task. To understand how popularity baseline is defined and evaluated, we sample 12 papers from top-tier conferences including KDD, WWW, SIGIR, and RecSys, and 6 open source toolkits. We note that the widely adopted MostPop baseline simply ranks items based on the number of interactions in the training data. We argue that the current evaluation of popularity (i) does not reflect the popular items at the time when a user interacts with the system, and (ii) may recommend items released after a user's last interaction with the system. On the widely used MovieLens dataset, we show that the performance of popularity could be significantly improved by 70% or more, if we consider the popular items at the time point when a user interacts with the system. We further show that, on MovieLens dataset, the users having lower tendencies on movies tend to follow the crowd and rate more popular movies. Movie lovers who rate a large number of movies, rate movies based on their own preferences and interests. Through this study, we call for a re-visit of the popularity baseline in recommender system to better reflect its effectiveness.Comment: Accepted by SIGIR202

An MLP-based Algorithm for Efficient Contrastive Graph Recommendations

Graph-based recommender systems (GBRSs) have achieved promising performance by incorporating the user-item bipartite graph using the Graph Neural Network (GNN). Among GBRSs, the information from each user and item's multi-hop neighbours is effectively conveyed between nodes through neighbourhood aggregation and message passing. Although effective, existing neighbourhood information aggregation and passing functions are usually computationally expensive. Motivated by the emerging contrastive learning technique, we design a simple neighbourhood construction method in conjunction with the contrastive objective function to simulate the neighbourhood information processing of GNN. In addition, we propose a simple algorithm based on Multilayer Perceptron (MLP) for learning users and items' representations with extra non-linearity while lowering computational burden compared with multi-layers GNNs. Our extensive empirical experiments on three public datasets demonstrate that our proposed model, i.e. MLP-CGRec, can reduce the GPU memory consumption and training time by up to 24.0% and 33.1%, respectively, without significantly degenerating the recommendation accuracy in comparison with competitive baselines

Toward a Better Understanding of Loss Functions for Collaborative Filtering

Collaborative filtering (CF) is a pivotal technique in modern recommender systems. The learning process of CF models typically consists of three components: interaction encoder, loss function, and negative sampling. Although many existing studies have proposed various CF models to design sophisticated interaction encoders, recent work shows that simply reformulating the loss functions can achieve significant performance gains. This paper delves into analyzing the relationship among existing loss functions. Our mathematical analysis reveals that the previous loss functions can be interpreted as alignment and uniformity functions: (i) the alignment matches user and item representations, and (ii) the uniformity disperses user and item distributions. Inspired by this analysis, we propose a novel loss function that improves the design of alignment and uniformity considering the unique patterns of datasets called Margin-aware Alignment and Weighted Uniformity (MAWU). The key novelty of MAWU is two-fold: (i) margin-aware alignment (MA) mitigates user/item-specific popularity biases, and (ii) weighted uniformity (WU) adjusts the significance between user and item uniformities to reflect the inherent characteristics of datasets. Extensive experimental results show that MF and LightGCN equipped with MAWU are comparable or superior to state-of-the-art CF models with various loss functions on three public datasets.Comment: Accepted by CIKM 202