Proceedings of the 16th ACM Conference on Recommender Systems最新文献_第3页

Reusable Self-Attention Recommender Systems in Fashion Industry Applications 时尚行业应用中可重用的自我关注推荐系统

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547377

Marjan Celikik, Ana Peleteiro-Ramallo, Jacek Wasilewski

引用次数: 2

Recommendations: They’re in fashion 建议:它们很流行

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547389

C. Carvalheira, Tiago Lacerda, Diogo Gonçalves

引用次数: 0

Learning to Ride a Buy-Cycle: A Hyper-Convolutional Model for Next Basket Repurchase Recommendation 学习驾驭买入周期:下一篮子回购推荐的超卷积模型

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3546763

Ori Katz, Oren Barkan, Noam Koenigstein, Nir Zabari

引用次数: 10

Long-term fairness for Group Recommender Systems with Large Groups 大群组群组推荐系统的长期公平性

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547424

Patrik Dokoupil

引用次数: 2

Dynamic Surrogate Switching: Sample-Efficient Search for Factorization Machine Configurations in Online Recommendations 动态代理交换:在线推荐中分解机配置的样本高效搜索

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547384

Blaž Škrlj, A. Schwartz, Jure Ferlez, Davorin Kopic, Naama Ziporin

{"title":"Dynamic Surrogate Switching: Sample-Efficient Search for Factorization Machine Configurations in Online Recommendations","authors":"Blaž Škrlj, A. Schwartz, Jure Ferlez, Davorin Kopic, Naama Ziporin","doi":"10.1145/3523227.3547384","DOIUrl":"https://doi.org/10.1145/3523227.3547384","url":null,"abstract":"Hyperparameter optimization is the process of identifying the appropriate hyperparameter configuration of a given machine learning model with regard to a given learning task. For smaller data sets, an exhaustive search is possible; However, when the data size and model complexity increase, the number of configuration evaluations becomes the main computational bottleneck. A promising paradigm for tackling this type of problem is surrogate-based optimization. The main idea underlying this paradigm considers an incrementally updated model of the relation between the hyperparameter space and the output (target) space; the data for this model are obtained by evaluating the main learning engine, which is, for example, a factorization machine-based model. By learning to approximate the hyperparameter-target relation, the surrogate (machine learning) model can be used to score large amounts of hyperparameter configurations, exploring parts of the configuration space beyond the reach of direct machine learning engine evaluation. Commonly, a surrogate is selected prior to optimization initialization and remains the same during the search. We investigated whether dynamic switching of surrogates during the optimization itself is a sensible idea of practical relevance for selecting the most appropriate factorization machine-based models for large-scale online recommendation. We conducted benchmarks on data sets containing hundreds of millions of instances against established baselines such as Random Forest- and Gaussian process-based surrogates. The results indicate that surrogate switching can offer good performance while considering fewer learning engine evaluations.","PeriodicalId":443279,"journal":{"name":"Proceedings of the 16th ACM Conference on Recommender Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128751699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DAGFiNN: A Conversational Conference Assistant 会话会议助理

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3551467

Ivica Kostric, K. Balog, Tølløv Alexander Aresvik, Nolwenn Bernard, Eyvinn Thu Dørheim, Pholit Hantula, Sander Havn-Sørensen, Rune Henriksen, Hengameh Hosseini, Ekaterina Khlybova, Weronika Lajewska, Sindre Ekrheim Mosand, Narmin Orujova

引用次数: 0

Two-Layer Bandit Optimization for Recommendations 推荐的双层强盗优化

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547396

Siyong Ma, Puja Das, S. M. Nikolakaki, Qifeng Chen, Humeyra Topcu Altintas

{"title":"Two-Layer Bandit Optimization for Recommendations","authors":"Siyong Ma, Puja Das, S. M. Nikolakaki, Qifeng Chen, Humeyra Topcu Altintas","doi":"10.1145/3523227.3547396","DOIUrl":"https://doi.org/10.1145/3523227.3547396","url":null,"abstract":"Online commercial app marketplaces serve millions of apps to billions of users in an efficient manner. Bandit optimization algorithms are used to ensure that the recommendations are relevant, and converge to the best performing content over time. However, directly applying bandits to real-world systems, where the catalog of items is dynamic and continuously refreshed, is not straightforward. One of the challenges we face is the existence of several competing content surfacing components, a phenomenon not unusual in large-scale recommender systems. This often leads to challenging scenarios, where improving the recommendations in one component can lead to performance degradation of another, i.e., “cannibalization”. To address this problem we introduce an efficient two-layer bandit approach which is contextualized to user cohorts of similar taste. We mitigate cannibalization at runtime within a single multi-intent content surfacing platform by formalizing relevant offline evaluation metrics, and by involving the cross-component interactions in the bandit rewards. The user engagement in our proposed system has more than doubled as measured by online A/B testings.","PeriodicalId":443279,"journal":{"name":"Proceedings of the 16th ACM Conference on Recommender Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132742521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Timely Personalization at Peloton: A System and Algorithm for Boosting Time-Relevant Content Peloton的及时个性化:一种促进时间相关内容的系统和算法

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547391

Shayak Banerjee, Vijay Pappu, N. Talukder, Shoya Yoshida, Arnab Bhadury, Allison Schloss, Jasmine Paulino

引用次数: 0

Towards Recommender Systems with Community Detection and Quantum Computing 基于社区检测和量子计算的推荐系统

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3551478

Riccardo Nembrini, Costantino Carugno, Maurizio Ferrari Dacrema, P. Cremonesi

{"title":"Towards Recommender Systems with Community Detection and Quantum Computing","authors":"Riccardo Nembrini, Costantino Carugno, Maurizio Ferrari Dacrema, P. Cremonesi","doi":"10.1145/3523227.3551478","DOIUrl":"https://doi.org/10.1145/3523227.3551478","url":null,"abstract":"After decades of being mainly confined to theoretical research, Quantum Computing is now becoming a useful tool for solving realistic problems. This work aims to experimentally explore the feasibility of using currently available quantum computers, based on the Quantum Annealing paradigm, to build a recommender system exploiting community detection. Community detection, by partitioning users and items into densely connected clusters, can boost the accuracy of non-personalized recommendation by assuming that users within each community share similar tastes. However, community detection is a computationally expensive process. The recent availability of Quantum Annealers as cloud-based devices, constitutes a new and promising direction to explore community detection, although effectively leveraging this new technology is a long-term path that still requires advancements in both hardware and algorithms. This work aims to begin this path by assessing the quality of community detection formulated as a Quadratic Unconstrained Binary Optimization problem on a real recommendation scenario. Results on several datasets show that the quantum solver is able to detect communities of comparable quality with respect to classical solvers, but with better speedup, and the non-personalized recommendation models built on top of these communities exhibit improved recommendation quality. The takeaway is that quantum computing, although in its early stages of maturity and applicability, shows promise in its ability to support new recommendation models and to bring improved scalability as technology evolves.","PeriodicalId":443279,"journal":{"name":"Proceedings of the 16th ACM Conference on Recommender Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128780989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Pursuing Optimal Trade-Off Solutions in Multi-Objective Recommender Systems 多目标推荐系统中最优权衡方案的研究

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547425

Vincenzo Paparella

{"title":"Pursuing Optimal Trade-Off Solutions in Multi-Objective Recommender Systems","authors":"Vincenzo Paparella","doi":"10.1145/3523227.3547425","DOIUrl":"https://doi.org/10.1145/3523227.3547425","url":null,"abstract":"Traditional research in Recommender Systems (RSs) often solely focuses on accuracy and a limited number of beyond-accuracy dimensions. Nonetheless, real-world RSs need to consider several other aspects, such as customer satisfaction or stakeholders’ interests. Consequently, the evaluation criteria must comprehend other dimensions, like click rate, or revenue, to cite a few of them. However, what objective should the system optimize, and what objective should it sacrifice? An emerging approach to tackle the problem and aim to blend different (sometimes conflicting) objectives is Multi-Objective Recommender Systems (MORSs). This proposal sketches a strategy to exploit the Pareto optimality to introduce a new optimal solution selection approach and investigate how existing RSs perform with multi-objective tasks. The goals are twofold: (i) discovering how to rank the solutions lying on the Pareto frontier to find the best trade-off solution and (ii) comparing the Pareto frontiers of different recommendation approaches to assess whether one performs better for the considered objectives. These measures could lead to a new class of MORSs that train an RS on multiple objectives to reach the best trade-off solution directly.","PeriodicalId":443279,"journal":{"name":"Proceedings of the 16th ACM Conference on Recommender Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115913389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1