Proceedings of the Recommender Systems Challenge 2022最新文献

RecSys Challenge 2022 Dataset: Dressipi 1M Fashion Sessions RecSys Challenge 2022数据集:Dressipi 1M时尚会议

Proceedings of the Recommender Systems Challenge 2022 Pub Date : 2022-09-18 DOI: 10.1145/3556702.3556779

Nick Landia, Rachael Mcalister, Donna North, Saikishore Kalloori, Abhishek Srivastava, B. Ferwerda

引用次数: 3

Simple and Efficient Recommendation Strategy for Warm/Cold Sessions for RecSys Challenge 2022 2022年RecSys挑战赛中简单有效的冷暖会话推荐策略

Proceedings of the Recommender Systems Challenge 2022 Pub Date : 2022-09-18 DOI: 10.1145/3556702.3556851

Hyunsung Lee, Sungwook Yoo, Andrew Yang, Wonjun Jang, Chiwan Park

引用次数: 0

Fashion Recommendation with a real Recommender System Flow 时尚推荐与一个真正的推荐系统流程

Proceedings of the Recommender Systems Challenge 2022 Pub Date : 2022-09-18 DOI: 10.1145/3556702.3556792

Qi Zhang, Guohao Cai, Wei Guo, Yiqiu Han, Zhenhua Dong, Ruiming Tang, Liangbi Li

引用次数: 0

A Diverse Models Ensemble for Fashion Session-Based Recommendation 基于时尚会议推荐的多样化模特组合

Proceedings of the Recommender Systems Challenge 2022 Pub Date : 2022-09-18 DOI: 10.1145/3556702.3556821

Benedikt D. Schifferer, Jiwei Liu, Sara Rabhi, Gilberto Titericz, Chris Deotte, Gabriel de Souza P. Moreira, Ronay Ak, Kazuki Onodera

引用次数: 2

United We Stand, Divided We Fall: Leveraging Ensembles of Recommenders to Compete with Budget Constrained Resources 团结则存，分裂则亡:利用推荐团队与预算有限的资源竞争

Proceedings of the Recommender Systems Challenge 2022 Pub Date : 2022-09-18 DOI: 10.1145/3556702.3556845

Pietro Maldini, Alessandro Sanvito, Mattia Surricchio

引用次数: 0

LightGBM using Enhanced and De-biased Item Representation for Better Session-based Fashion Recommender Systems LightGBM使用增强和去偏见的物品表示，以更好地基于会话的时尚推荐系统

Proceedings of the Recommender Systems Challenge 2022 Pub Date : 2022-09-18 DOI: 10.1145/3556702.3556839

Jiangwei Luo, Wenxuan Zhao, Ye Tang, Zhou Zhou, Huimin Xiong, Zhulin Tao

{"title":"LightGBM using Enhanced and De-biased Item Representation for Better Session-based Fashion Recommender Systems","authors":"Jiangwei Luo, Wenxuan Zhao, Ye Tang, Zhou Zhou, Huimin Xiong, Zhulin Tao","doi":"10.1145/3556702.3556839","DOIUrl":"https://doi.org/10.1145/3556702.3556839","url":null,"abstract":"In this paper, we present our 5th place solution for the ACM RecSys 2022 challenge (http://www.recsyschallenge.com/2022/).The competition, organized by Dressipi, aims to predict the fashion item purchasing actions on a public dataset of 1.1 million online retail sessions. In the traditional sequence recommendation model, we mainly utilize the action sequence information to model the representations of items and users. However, the fashion categories and features of items change much more frequently in our task, and the bias caused by the popularity will greatly affect the representations learning for the users and items. In this work, our team, termed THLUO, devise a model, which injects the spatiotemporal features of each item in sessions to alleviate the bias problem and capture the latest fashion trend information hiding in the session. In more detail, we proposed a two stages model, which includes retrieval and re-ranking. In the retrieval stage, we adapt the positions and timestamp features into the item-CF model to eliminate the bias caused by the popularity. In the re-ranking stage, we not only adapt traditional feature engineering but also used the enhanced features created by neural net works and fusion them as inputs of LightGBM for final prediction. After careful experiments, our model’s result archive an outstanding score of 0.2062 in mean reciprocal rank metrics in the test dataset, finally ranked fifth in the competition.","PeriodicalId":141185,"journal":{"name":"Proceedings of the Recommender Systems Challenge 2022","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114582717","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: 4

Lightweight Model for Session-Based Recommender Systems with Seasonality Information in the Fashion Domain 基于会话的时尚领域季节性信息推荐系统的轻量级模型

Proceedings of the Recommender Systems Challenge 2022 Pub Date : 2022-09-18 DOI: 10.1145/3556702.3556829

Nicola Della Volpe, Lorenzo Mainetti, Alessio Martignetti, Andrea Menta, Riccardo Pala, Giacomo Polvanesi, Francesco Sammarco, Fernando Benjamin Perez Maurera, Cesare Bernardis, Maurizio Ferrari Dacrema

引用次数: 0

Session-Based Recommendation by combining Probabilistic Models and LSTM 结合概率模型和LSTM的基于会话的推荐

Proceedings of the Recommender Systems Challenge 2022 Pub Date : 2022-09-18 DOI: 10.1145/3556702.3556846

C. Panagiotakis, H. Papadakis

{"title":"Session-Based Recommendation by combining Probabilistic Models and LSTM","authors":"C. Panagiotakis, H. Papadakis","doi":"10.1145/3556702.3556846","DOIUrl":"https://doi.org/10.1145/3556702.3556846","url":null,"abstract":"In this paper, we present the approach, we used as team ”DataLab HMU.GR”, for the ACM RecSys Challenge 2022 [1]. The challenge aims to predict the item that was purchased for a given sequence of item views (session). The full dataset, provided by Dressipi, consists of 1.1 million online retail sessions. Our proposed method, that solves the Session-Based Recommendation problem, relies on an efficient deterministic system based on a weighted combination of Probabilistic models and an LSTM neural network. Probabilistic models learn the transition probabilities between item-item interactions of each session, that are used to predict the purchase probability of an item in a new session. The LSTM neural network takes as input the context representation of the items in a session and a candidate item and predicts the purchase probability of the candidate item. The experimental results demonstrate the high performance and the computational efficiency of the probabilistic models. Our submission achieved the 13th rank and an overall score of 0.1963 in the final competition results. We release our source code at: https://github.com/cpanag79/recsys-Challenge-2022.","PeriodicalId":141185,"journal":{"name":"Proceedings of the Recommender Systems Challenge 2022","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121773133","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

SIHG4SR: Side Information Heterogeneous Graph for Session Recommender 会话推荐的侧信息异构图

Proceedings of the Recommender Systems Challenge 2022 Pub Date : 2022-09-18 DOI: 10.1145/3556702.3556852

Chendi Xue, Xinyao Wang, Yu Zhou, Ke Ding, Jian Zhang, Rita Brugarolas Brufau, Eric Anderson

引用次数: 0

Session-based Recommendation with Transformers 基于会话的变压器推荐

Proceedings of the Recommender Systems Challenge 2022 Pub Date : 2022-09-18 DOI: 10.1145/3556702.3556844

Yichao Lu, Jianing Sun

引用次数: 3