Some Economic Effects of Changes to Gate-Sharing Arrangements in the Australian Football League

Whilst gate revenue as a source of revenue for the (member-owned win-maximising) clubs in the Australian Football League (AFL) is relatively small and declining as a proportion, it is still an important source of revenue difference between clubs, and potentially their on-field playing performance. Until 2000, gate revenue was shared between the home and away teams (after the deduction of match expenses), after which the policy was changed to allow the home team to keep all of the (net) gate receipts. In the AFL, membership income, reserved seat and corporate box income has never been shared, but the league does share the revenue from key income streams such as national TV broadcast rights (there is no local TV revenue), corporate sponsorship and finals.

Equivariant Contrastive Learning for Sequential Recommendation

Contrastive learning (CL) benefits the training of sequential recommendation models with informative self-supervision signals. Existing solutions apply general sequential data augmentation strategies to generate positive pairs and encourage their representations to be invariant. However, due to the inherent properties of user behavior sequences, some augmentation strategies, such as item substitution, can lead to changes in user intent. Learning indiscriminately invariant representations for all augmentation strategies might be suboptimal. Therefore, we propose Equivariant Contrastive Learning for Sequential Recommendation (ECL-SR), which endows SR models with great discriminative power, making the learned user behavior representations sensitive to invasive augmentations (e.g., item substitution) and insensitive to mild augmentations (e.g., featurelevel dropout masking). In detail, we use the conditional discriminator to capture differences in behavior due to item substitution, which encourages the user behavior encoder to be equivariant to invasive augmentations. Comprehensive experiments on four benchmark datasets show that the proposed ECL-SR framework achieves competitive performance compared to state-of-the-art SR models. The source code is available at https://github.com/Tokkiu/ECL.Comment: Accepted by RecSys 202

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

Twenty Five Years of Asymptotic Freedom

On the occasion of the 25th anniversary of Asymptotic Freedom, celebrated at the QCD Euroconference 98 on Quantum Chromodynamics, Montpellier, July 1998, I described the discovery of Asymptotic Freedom and the emergence of QCD.Comment: 35 pages, LaTe

Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems

In recent years, algorithm research in the area of recommender systems has shifted from matrix factorization techniques and their latent factor models to neural approaches. However, given the proven power of latent factor models, some newer neural approaches incorporate them within more complex network architectures. One specific idea, recently put forward by several researchers, is to consider potential correlations between the latent factors, i.e., embeddings, by applying convolutions over the user-item interaction map. However, contrary to what is claimed in these articles, such interaction maps do not share the properties of images where Convolutional Neural Networks (CNNs) are particularly useful. In this work, we show through analytical considerations and empirical evaluations that the claimed gains reported in the literature cannot be attributed to the ability of CNNs to model embedding correlations, as argued in the original papers. Moreover, additional performance evaluations show that all of the examined recent CNN-based models are outperformed by existing non-neural machine learning techniques or traditional nearest-neighbor approaches. On a more general level, our work points to major methodological issues in recommender systems research.Comment: Source code available here: https://github.com/MaurizioFD/RecSys2019_DeepLearning_Evaluatio