Proceedings of the 16th ACM Conference on Recommender Systems最新文献_第5页

MARRS: A Framework for multi-objective risk-aware route recommendation using Multitask-Transformer 基于Multitask-Transformer的多目标风险感知路径推荐框架

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3546787

Bhumika, D. Das

引用次数: 2

Enhancing Counterfactual Evaluation and Learning for Recommendation Systems 增强推荐系统的反事实评估和学习

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547429

Nicolò Felicioni

引用次数: 3

RepSys: Framework for Interactive Evaluation of Recommender Systems RepSys:推荐系统互动评估框架

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3551469

J. Safarik, Vojtěch Vančura, P. Kordík

引用次数: 2

BRUCE: Bundle Recommendation Using Contextualized item Embeddings 布鲁斯:使用情境化项目嵌入的捆绑推荐

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3546754

Tzoof Avny Brosh, Amit Livne, Oren Sar Shalom, Bracha Shapira, Mark Last

{"title":"BRUCE: Bundle Recommendation Using Contextualized item Embeddings","authors":"Tzoof Avny Brosh, Amit Livne, Oren Sar Shalom, Bracha Shapira, Mark Last","doi":"10.1145/3523227.3546754","DOIUrl":"https://doi.org/10.1145/3523227.3546754","url":null,"abstract":"A bundle is a pre-defined set of items that are collected together. In many domains, bundling is one of the most important marketing strategies for item promotion, commonly used in e-commerce. Bundle recommendation resembles the item recommendation task, where bundles are the recommended unit, but it poses additional challenges; while item recommendation requires only user and item understanding, bundle recommendation also requires modeling the connections between the various items in a bundle. Transformers have driven the state-of-the-art methods for set and sequence modeling in various natural language processing and computer vision tasks, emphasizing the understanding that the neighbors of an element are of crucial importance. Under some required adjustments, we believe the same applies for items in bundles, and better capturing the relations of an item with other items in the bundle may lead to improved recommendations. To address that, we introduce BRUCE - a novel model for bundle recommendation, in which we adapt Transformers to represent data on users, items, and bundles. This allows exploiting the self-attention mechanism to model the following: latent relations between the items in a bundle; and users’ preferences toward each of the items in the bundle and toward the whole bundle. Moreover, we examine various architectures to integrate the items’ and the users’ information and provide insights on architecture selection based on data characteristics. Experiments conducted on three benchmark datasets show that the proposed approach contributes to the accuracy of the recommendation and substantially outperforms state-of-the-art methods","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":"122680665","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: 9

Scalable Linear Shallow Autoencoder for Collaborative Filtering 用于协同滤波的可扩展线性浅自编码器

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3551482

Vojtěch Vančura, Rodrigo Alves, Petr Kasalický, P. Kordík

{"title":"Scalable Linear Shallow Autoencoder for Collaborative Filtering","authors":"Vojtěch Vančura, Rodrigo Alves, Petr Kasalický, P. Kordík","doi":"10.1145/3523227.3551482","DOIUrl":"https://doi.org/10.1145/3523227.3551482","url":null,"abstract":"Recently, the RS research community has witnessed a surge in popularity for shallow autoencoder-based CF methods. Due to its straightforward implementation and high accuracy on item retrieval metrics, EASE is potentially the most prominent of these models. Despite its accuracy and simplicity, EASE cannot be employed in some real-world recommender system applications due to its inability to scale to huge interaction matrices. In this paper, we proposed ELSA, a scalable shallow autoencoder method for implicit feedback recommenders. ELSA is a scalable autoencoder in which the hidden layer is factorizable into a low-rank plus sparse structure, thereby drastically lowering memory consumption and computation time. We conducted a comprehensive offline experimental section that combined synthetic and several real-world datasets. We also validated our strategy in an online setting by comparing ELSA to baselines in a live recommender system using an A/B test. Experiments demonstrate that ELSA is scalable and has competitive performance. Finally, we demonstrate the explainability of ELSA by illustrating the recovered latent space.","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":"132414052","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: 9

CARS: Workshop on Context-Aware Recommender Systems 2022 汽车:情景感知推荐系统研讨会2022

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547421

G. Adomavicius, Konstantin Bauman, B. Mobasher, Francesco Ricci, Alexander Tuzhilin, Moshe Unger

引用次数: 1

The Effect of Feedback Granularity on Recommender Systems Performance 反馈粒度对推荐系统性能的影响

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3551479

Ladislav Peška, Stepán Balcar

{"title":"The Effect of Feedback Granularity on Recommender Systems Performance","authors":"Ladislav Peška, Stepán Balcar","doi":"10.1145/3523227.3551479","DOIUrl":"https://doi.org/10.1145/3523227.3551479","url":null,"abstract":"The main source of knowledge utilized in recommender systems (RS) is users’ feedback. While the usage of implicit feedback (i.e. user’s behavior statistics) is gaining in prominence, the explicit feedback (i.e. user’s ratings) remain an important data source. This is true especially for domains, where evaluation of an object does not require an extensive usage and users are well motivated to do so (e.g., video-on-demand services or library archives). So far, numerous rating schemes for explicit feedback have been proposed, ranging both in granularity and presentation style. There are several works studying the effect of rating’s scale and presentation on user’s rating behavior, e.g. willingness to provide feedback or various biases in rating behavior. Nonetheless, the effect of ratings granularity on RS performance remain largely under-researched. In this paper, we studied the combined effect of ratings granularity and supposed probability of feedback existence on various performance statistics of recommender systems. Results indicate that decreasing feedback granularity may lead to changes in RS’s performance w.r.t. nDCG for some recommending algorithms. Nonetheless, in most cases the effect of feedback granularity is surpassed by even a small decrease in feedback’s quantity. Therefore, our results corroborate the policy of many major real-world applications, i.e. preference of simpler rating schemes with the higher chance of feedback reception instead of finer-grained rating scenarios.","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":"126898195","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

Off-Policy Actor-critic for Recommender Systems 推荐系统的非政策行为者批评家

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3546758

Minmin Chen, Can Xu, Vince Gatto, Devanshu Jain, Aviral Kumar, Ed H. Chi

{"title":"Off-Policy Actor-critic for Recommender Systems","authors":"Minmin Chen, Can Xu, Vince Gatto, Devanshu Jain, Aviral Kumar, Ed H. Chi","doi":"10.1145/3523227.3546758","DOIUrl":"https://doi.org/10.1145/3523227.3546758","url":null,"abstract":"Industrial recommendation platforms are increasingly concerned with how to make recommendations that cause users to enjoy their long term experience on the platform. Reinforcement learning emerged naturally as an appealing approach for its promise in 1) combating feedback loop effect resulted from myopic system behaviors; and 2) sequential planning to optimize long term outcome. Scaling RL algorithms to production recommender systems serving billions of users and contents, however remain challenging. Sample inefficiency and instability of online RL hinder its widespread adoption in production. Offline RL enables usage of off-policy data and batch learning. It on the other hand faces significant challenges in learning due to the distribution shift. A REINFORCE agent [3] was successfully tested for YouTube recommendation, significantly outperforming a sophisticated supervised learning production system. Off-policy correction was employed to learn from logged data. The algorithm partially mitigates the distribution shift by employing a one-step importance weighting. We resort to the off-policy actor critic algorithms to addresses the distribution shift to a better extent. Here we share the key designs in setting up an off-policy actor-critic agent for production recommender systems. It extends [3] with a critic network that estimates the value of any state-action pairs under the target learned policy through temporal difference learning. We demonstrate in offline and live experiments that the new framework out-performs baseline and improves long term user experience. An interesting discovery along our investigation is that recommendation agents that employ a softmax policy parameterization, can end up being too pessimistic about out-of-distribution (OOD) actions. Finding the right balance between pessimism and optimism on OOD actions is critical to the success of offline RL for recommender systems.","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":"125379971","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: 19

RecWork: Workshop on Recommender Systems for the Future of Work RecWork:未来工作推荐系统研讨会

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547415

J. Konstan, A. Muralidharan, Ankan Saha, Shilad Sen, Mengting Wan, Longqi Yang

引用次数: 1

Who do you think I am? Interactive User Modelling with Item Metadata 你以为我是谁?具有项目元数据的交互式用户建模

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3551470

Joey De Pauw, Koen Ruymbeek, Bart Goethals

{"title":"Who do you think I am? Interactive User Modelling with Item Metadata","authors":"Joey De Pauw, Koen Ruymbeek, Bart Goethals","doi":"10.1145/3523227.3551470","DOIUrl":"https://doi.org/10.1145/3523227.3551470","url":null,"abstract":"Recommender systems are used in many different applications and contexts, however their main goal can always be summarised as “connecting relevant content to interested users”. Explanations have been found to help recommender systems achieve this goal by giving users a look under the hood that helps them understand why they are recommended certain items. Furthermore, explanations can be considered to be the first step towards interacting with the system. Indeed, for a user to give feedback and guide the system towards better understanding her preferences, it helps if the user has a better idea of what the system has already learned. To this end, we propose a linear collaborative filtering recommendation model that builds user profiles within the domain of item metadata. Our method is hence inherently transparent and explainable. Moreover, since recommendations are computed as a linear function of item metadata and the interpretable user profile, our method seamlessly supports interactive recommendation. In other words, users can directly tweak the weights of the learned profile for more fine-grained browsing and discovery of content based on their current interests. We demonstrate the interactive aspect of this model in an online application for discovering cultural events in Belgium.","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":"116371430","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