Session 8: Algorithmic Advances

Date: Wednesday 16:00-17:30 CET
Chair: Paolo Cremonesi (Politecnico di Milano)

  • PANext-item Recommendations in Short Sessions
    by Wenzhuo Song (Jilin University, China), Shoujin Wang (Macquarie University, Australia), Yan Wang (Macquarie University, Australia), and Shengsheng Wang (Jilin University, China)

    The changing preferences of users towards items trigger the emergence of session-based recommender systems (SBRSs), which aim to model the dynamic preferences of users for next-item recommendations. However, most of the existing studies on SBRSs are based on long sessions only for recommendations, ignoring short sessions, though short sessions, in fact, account for a large proportion in most of the real-world datasets. As a result, the applicability of existing SBRSs solutions is greatly reduced. In a short session, quite limited contextual information is available, making the next-item recommendation very challenging. To this end, in this paper, inspired by the success of few-shot learning (FSL) in effectively learning a model with limited instances, we formulate the next-item recommendation as an FSL problem. Accordingly, following the basic idea of a representative approach for FSL, i.e., meta-learning, we devise an effective SBRS called INter-SEssion collaborativeRecommender neTwork (INSERT)
    for next-item recommendations in short sessions. With the carefully devised local module and global module, INSERT is able to learn an optimal preference representation of the current user in a given short session. In particular, in the global module, a similar session retrieval network (SSRN) is designed to find out the sessions similar to the current short session from the historical sessions of both the current user and other users, respectively. The obtained similar sessions are then utilized to complement and optimize the preference representation learned from the current short session by the local module for more accurate next-item recommendations in this short session. Extensive experiments conducted on two real-world datasets demonstrate the superiority of our proposed INSERT over the state-of-the-art SBRSs when making next-item recommendations in short sessions.

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  • PABurst-induced Multi-Armed Bandit for Learning Recommendation
    by Rodrigo Alves (TU Kaiserslautern, Germany), Antoine Ledent (TU Kaiserslautern, Germany), and Marius Kloft (TU Kaiserslautern, Germany)

    In this paper, we introduce a non-stationary and context-free Multi-Armed Bandit (MAB) problem and a novel algorithm (which we refer to as BMAB) to solve it. The problem is context-free in the sense that no side information about users or items is needed. We work in a continuous-time setting where each timestamp corresponds to a visit by a user and a corresponding decision regarding recommendation. The main novelty is that we model the reward distribution as a consequence of variations in the intensity of the activity, and thereby we assist the exploration/exploitation dilemma by exploring the temporal dynamics of the audience. To achieve this, we assume that the recommendation procedure can be split into two different states: the loyal and the curious state. We identify the current state by modelling the events as a mixture of two Poisson processes, one for each of the possible states. We further assume that the loyal audience is associated with a single stationary reward distribution, but each bursty period comes with its own reward distribution. We test our algorithm and compare it to several baselines in two strands of experiments: synthetic data simulations and real-world datasets. The results demonstrate that BMAB achieves competitive results when compared to state-of-the-art methods.

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  • PAHierarchical Latent Relation Modeling for Collaborative Metric Learning
    by Viet-Anh Tran (Deezer, France), Guillaume Salha-Galvan (Deezer, France), Romain Hennequin (Deezer, France), and Manuel Moussallam (Deezer, France)

    Collaborative Metric Learning (CML) recently emerged as a powerful paradigm for recommendation based on implicit feedback collaborative filtering. However, standard CML methods learn fixed user and item representations, which fails to capture the complex interests of users. Existing extensions of CML also either ignore the heterogeneity of user-item relations, i.e. that a user can simultaneously like very different items, or the latent item-item relations, i.e. that a user’s preference for an item depends, not only on its intrinsic characteristics, but also on items they previously interacted with. In this paper, we present a hierarchical CML model that jointly captures latent user-item and item-item relations from implicit data. Our approach is inspired by translation mechanisms from knowledge graph embedding and leverages memory-based attention networks. We empirically show the relevance of this joint relational modeling, by outperforming existing CML models on recommendation tasks on several real-world datasets. Our experiments also emphasize the limits of current CML relational models on very sparse datasets.

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  • REPA Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models
    by Alexander Dallmann (Data Science Chair University of Würzburg, Germany), Daniel Zoller (Data Science Chair University of Würzburg, Germany), and Andreas Hotho (Data Science Chair University of Würzburg, Germany)

    At the present time, sequential item recommendation models are compared by calculating metrics on a small item subset (target 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 full ranking, that is, 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 full 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 full 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 full 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 full ranking of recommender models and therefore both should be avoided in favor of the full ranking when establishing state-of-the-art.

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  • PATop-K Contextual Bandits with Equity of Exposure
    by Olivier Jeunen (University of Antwerp, Belgium) and Bart Goethals (University of Antwerp, Belgium)

    The contextual bandit paradigm provides a general framework for decision-making under uncertainty. It is theoretically well-defined and well-studied, and many personalisation use-cases can be cast as a bandit learning problem. Because this allows for the direct optimisation of utility metrics that rely on online interventions (such as click-through-rate (CTR)), this framework has become an attractive choice to practitioners. Historically, the literature on this topic has focused on a one-sided, user-focused notion of utility, overall disregarding the perspective of content providers in online marketplaces (for example, musical artists on streaming services). If not properly taken into account – recommendation systems in such environments are known to lead to unfair distributions of attention and exposure, which can directly affect the income of the providers. Recent work has shed a light on this, and there is now a growing consensus that some notion of “equity of exposure” might be preferable to implement in many recommendation use-cases.
    We study how the top-K contextual bandit problem relates to issues of disparate exposure, and how this disparity can be minimised. The predominant approach in practice is to greedily rank the top-K items according to their estimated utility, as this is optimal according to the well-known Probability Ranking Principle. Instead, we introduce a configurable tolerance parameter that defines an acceptable decrease in utility for a maximal increase in fairness of exposure. We propose a personalised exposure-aware arm selection algorithm that handles this relevance-fairness trade-off on a user-level, as recent work suggests that users’ openness to randomisation may vary greatly over the global populace. Our model-agnostic algorithm deals with arm selection instead of utility modelling, and can therefore be implemented on top of any existing bandit system with minimal changes. We conclude with a case study on carousel personalisation in music recommendation: empirical observations highlight the effectiveness of our proposed method and show that exposure disparity can be significantly reduced with a negligible impact on user utility.

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