Proceedings of the 16th ACM Conference on Recommender Systems最新文献_第6页

Self-Supervised Bot Play for Transcript-Free Conversational Recommendation with Rationales 基于基本原理的无转录会话推荐的自我监督Bot游戏

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3546783

Shuyang Li, Bodhisattwa Prasad Majumder, Julian McAuley

{"title":"Self-Supervised Bot Play for Transcript-Free Conversational Recommendation with Rationales","authors":"Shuyang Li, Bodhisattwa Prasad Majumder, Julian McAuley","doi":"10.1145/3523227.3546783","DOIUrl":"https://doi.org/10.1145/3523227.3546783","url":null,"abstract":"Conversational recommender systems offer a way for users to engage in multi-turn conversations to find items they enjoy. For users to trust an agent and give effective feedback, the recommender system must be able to explain its suggestions and rationales. We develop a two-part framework for training multi-turn conversational recommenders that provide recommendation rationales that users can effectively interact with to receive better recommendations. First, we train a recommender system to jointly suggest items and explain its reasoning via subjective rationales. We then fine-tune this model to incorporate iterative user feedback via self-supervised bot-play. Experiments on three real-world datasets demonstrate that our system can be applied to different recommendation models across diverse domains to achieve state-of-the-art performance in multi-turn recommendation. Human studies show that systems trained with our framework provide more useful, helpful, and knowledgeable suggestions in warm- and cold-start settings. Our framework allows us to use only product reviews during training, avoiding the need for expensive dialog transcript datasets that limit the applicability of previous conversational recommender agents.","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":"116414532","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: 3

Reducing Cross-Topic Political Homogenization in Content-Based News Recommendation 减少基于内容的新闻推荐中的跨话题政治同质化

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3546782

K. Shivaram, Ping Liu, Matthew Shapiro, M. Bilgic, A. Culotta

引用次数: 0

Multiobjective Evaluation of Reinforcement Learning Based Recommender Systems 基于强化学习的推荐系统多目标评价

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3551485

A. Grishanov, A. Ianina, K. Vorontsov

引用次数: 0

A Lightweight Transformer for Next-Item Product Recommendation 用于下一项产品推荐的轻量级变压器

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547491

M. J. Mei, Cole Zuber, Y. Khazaeni

引用次数: 5

Hands on Explainable Recommender Systems with Knowledge Graphs 使用知识图谱的可解释推荐系统

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547374

Giacomo Balloccu, Ludovico Boratto, G. Fenu, M. Marras

引用次数: 4

You Say Factorization Machine, I Say Neural Network - It’s All in the Activation 你说分解机器，我说神经网络——都在激活中

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3551499

Chen Almagor, Yedid Hoshen

引用次数: 1

Building and Deploying a Multi-Stage Recommender System with Merlin 用Merlin构建和部署一个多级推荐系统

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3551468

Karl Higley, Even Oldridge, Ronay Ak, Sara Rabhi, G. Moreira

{"title":"Building and Deploying a Multi-Stage Recommender System with Merlin","authors":"Karl Higley, Even Oldridge, Ronay Ak, Sara Rabhi, G. Moreira","doi":"10.1145/3523227.3551468","DOIUrl":"https://doi.org/10.1145/3523227.3551468","url":null,"abstract":"Newcomers to recommender systems often face challenges related to their lack of understanding of how these systems operate in real life. In most online content related to this topic, the focus is on models and algorithms that score items based on the user’s preferences. However, the recommender model alone does not comprise everything needed for serving optimized recommender systems that meet the company’s business objectives. An industry-standard recommender system involves a number of steps, including data preprocessing, defining and training recommender models, as well as filtering and business logic for serving. In this work, we propose the four-stage recommender system, an industry-wide design pattern we have identified for production recommender systems. The four-stage pipeline includes an item retrieval step that prepares a small subset of relevant items for scoring. The filtering stage then cleans up the subset of items based on business logic such as removing out-of-stock or previously seen items. As for the ranking component, it uses a recommender model to score each item in the presented list based on the preferences of the user. In the final step, the scores are re-ordered to provide a final recommendation list aligned with other business needs or constraints such as diversity. In particular, the presented demo demonstrates how easy it is to build and deploy a four-stage recommender system pipeline using the NVIDIA Merlin open-source framework.","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":"115279616","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: 7

Estimating Long-term Effects from Experimental Data 从实验数据估计长期影响

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547398

Ziyang Tang, Yiheng Duan, Steven H. Zhu, Stephanie S. Zhang, Lihong Li

引用次数: 1

Global and Personalized Graphs for Heterogeneous Sequential Recommendation by Learning Behavior Transitions and User Intentions 基于学习行为转换和用户意图的异构顺序推荐的全局和个性化图

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3546761

Weixin Chen, Mingkai He, Yongxin Ni, Weike Pan, L. Chen, Zhong Ming

{"title":"Global and Personalized Graphs for Heterogeneous Sequential Recommendation by Learning Behavior Transitions and User Intentions","authors":"Weixin Chen, Mingkai He, Yongxin Ni, Weike Pan, L. Chen, Zhong Ming","doi":"10.1145/3523227.3546761","DOIUrl":"https://doi.org/10.1145/3523227.3546761","url":null,"abstract":"Heterogeneous sequential recommendation (HSR) is a very important recommendation problem, which aims to predict a user’s next interacted item under a target behavior type (e.g., purchase in e-commerce sites) based on his/her historical interactions with different behaviors. Though existing sequential methods have achieved advanced performance by considering the varied impacts of interactions with sequential information, a large body of them still have two major shortcomings. Firstly, they usually model different behaviors separately without considering the correlations between them. The transitions from item to item under diverse behaviors indicate some users’ potential behavior manner. Secondly, though the behavior information contains a user’s fine-grained interests, the insufficient consideration of the local context information limits them from well understanding user intentions. Utilizing the adjacent interactions to better understand a user’s behavior could improve the certainty of prediction. To address these two issues, we propose a novel solution utilizing global and personalized graphs for HSR (GPG4HSR) to learn behavior transitions and user intentions. Specifically, our GPG4HSR consists of two graphs, i.e., a global graph to capture the transitions between different behaviors, and a personalized graph to model items with behaviors by further considering the distinct user intentions of the adjacent contextually relevant nodes. Extensive experiments on four public datasets with the state-of-the-art baselines demonstrate the effectiveness and general applicability of our method GPG4HSR.","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":"121157522","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: 5

Hands-on Reinforcement Learning for Recommender Systems - From Bandits to SlateQ to Offline RL with Ray RLlib 推荐系统的动手强化学习-从Bandits到SlateQ到离线RL与Ray RLlib

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547370

Christy D. Bergman, Kourosh Hakhamaneshi

引用次数: 0