发布求助
文献互助 智能选刊 最新文献

Proceedings of the 2015 International ACM Recommender Systems Challenge最新文献

筛选
英文 中文
E-Commerce Item Recommendation Based on Field-aware Factorization Machine 基于现场感知分解机的电子商务商品推荐
Proceedings of the 2015 International ACM Recommender Systems Challenge Pub Date : 2015-09-16 DOI: 10.1145/2813448.2813511
Peng Yan, Xiaocong Zhou, Yitao Duan
{"title":"E-Commerce Item Recommendation Based on Field-aware Factorization Machine","authors":"Peng Yan, Xiaocong Zhou, Yitao Duan","doi":"10.1145/2813448.2813511","DOIUrl":"https://doi.org/10.1145/2813448.2813511","url":null,"abstract":"The RecSys 2015 contest [1] seeks the best solution to a top-N e-commerce item recommendation problem. This paper describes the team Random Walker's approach to this challenge, which won the 3rd place in the contest. Our solution consists of the following components. Firstly, we cast the top-N recommendation task into a binary classification problem and extract original features from the raw data. Secondly, we learn derived features using field-aware factorization machines (FFM) and gradient boosting decision tree (GBDT). Lastly, we train 2 FFM models with different feature sets and combine them by a non-linear weighted blending. This solution is the result of numerous tests and the scheme turns out to be effective. Our final solution achieved a score of 61075.2, ranking in the third place on the public leaderboard.","PeriodicalId":324873,"journal":{"name":"Proceedings of the 2015 International ACM Recommender Systems Challenge","volume":"IA-21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126562604","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
Neural Modeling of Buying Behaviour for E-Commerce from Clicking Patterns 基于点击模式的电子商务购买行为神经模型
Proceedings of the 2015 International ACM Recommender Systems Challenge Pub Date : 2015-09-16 DOI: 10.1145/2813448.2813521
Zhenzhou Wu, Bao Hong Tan, Rubing Duan, Yong Liu, R. Goh
{"title":"Neural Modeling of Buying Behaviour for E-Commerce from Clicking Patterns","authors":"Zhenzhou Wu, Bao Hong Tan, Rubing Duan, Yong Liu, R. Goh","doi":"10.1145/2813448.2813521","DOIUrl":"https://doi.org/10.1145/2813448.2813521","url":null,"abstract":"In our study, we investigate the effectiveness of different models to the purchasing behaviour at YOOCHOOSE website. This paper provide a direct method in modeling the buying pattern in a clicking session by simply using the time-stamp of the clicks and show that the result is comparable to using more massive feature engineering that requires session summarizing. Our proposed method requires much lesser feature engineering and more natural modeling of the click events directly in a typical purchasing session in e-commerce.","PeriodicalId":324873,"journal":{"name":"Proceedings of the 2015 International ACM Recommender Systems Challenge","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131236835","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: 26
Solving RecSys Challenge 2015 by Linear Models, Gradient Boosted Trees and Metric Optimization 通过线性模型、梯度增强树和度量优化解决RecSys挑战2015
Proceedings of the 2015 International ACM Recommender Systems Challenge Pub Date : 2015-09-16 DOI: 10.1145/2813448.2813513
Róbert Pálovics, Peter Szalai, Levente Kocsis, A. Szabó, Erzsébet Frigó, Júlia Pap, Zsófia K. Nyikes, A. Benczúr
{"title":"Solving RecSys Challenge 2015 by Linear Models, Gradient Boosted Trees and Metric Optimization","authors":"Róbert Pálovics, Peter Szalai, Levente Kocsis, A. Szabó, Erzsébet Frigó, Júlia Pap, Zsófia K. Nyikes, A. Benczúr","doi":"10.1145/2813448.2813513","DOIUrl":"https://doi.org/10.1145/2813448.2813513","url":null,"abstract":"The RecSys Challenge 2015 task requested prediction for items purchased in online web shop sessions. We describe our method that reached fifth place on the leaderboard by constructing a large number of item, session, and session-item features and using linear models and gradient boosted trees for learning. An important element of our method included optimization for the specific evaluation metric.","PeriodicalId":324873,"journal":{"name":"Proceedings of the 2015 International ACM Recommender Systems Challenge","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116182417","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
Purchase Prediction and Item Suggestion based on HTTP sessions in absence of User Information 在没有用户信息的情况下，基于HTTP会话的购买预测和商品建议
Proceedings of the 2015 International ACM Recommender Systems Challenge Pub Date : 2015-09-16 DOI: 10.1145/2813448.2813515
Pouya Esmailian, M. Jalili
{"title":"Purchase Prediction and Item Suggestion based on HTTP sessions in absence of User Information","authors":"Pouya Esmailian, M. Jalili","doi":"10.1145/2813448.2813515","DOIUrl":"https://doi.org/10.1145/2813448.2813515","url":null,"abstract":"In this paper, the task is to determine whether an HTTP session buys an item, or not, and if so, which items will be purchased. An HTTP session is a series of item clicks. A session has type buy, if it buys at least one item, or non-buy otherwise. Accordingly, data is in (session, item, time) format, which tells us when an item is clicked or purchased during an HTTP session. The main challenge comes from the fact that (1) user information is not available for clicked or purchased items, which are merely tagged with anonymous sessions, and (2) suggestions are highly temporal as they are suggested to sessions instead of users. In other words, users which are stable and identified are replaced with sessions which are temporal and anonymous. In this work, we propose a feature-based system that predicts the type of a session, and determines which items are going to be purchased. As the main contribution, we have modeled sessions separated by the number of unique items, prioritized item-features based on the number of clicks, and utilized cumulative statistics of similar items to attenuate the sparsity problem.","PeriodicalId":324873,"journal":{"name":"Proceedings of the 2015 International ACM Recommender Systems Challenge","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114078225","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
Proceedings of the 2015 International ACM Recommender Systems Challenge 2015年国际ACM推荐系统挑战赛论文集
Proceedings of the 2015 International ACM Recommender Systems Challenge Pub Date : 2015-09-16 DOI: 10.1145/2813448
David Ben-Shimon, Michael Friedmann, L. Rokach, Bracha Shapira
{"title":"Proceedings of the 2015 International ACM Recommender Systems Challenge","authors":"David Ben-Shimon, Michael Friedmann, L. Rokach, Bracha Shapira","doi":"10.1145/2813448","DOIUrl":"https://doi.org/10.1145/2813448","url":null,"abstract":"This volume contains the papers presented at the ACM RecSys Challenge 2015 workshop held on September 16, 2015, in Vienna, Austria. The challenge offered participants the opportunity to work on a large-scale e-commerce dataset from a big retailer in Europe. Participants tackled the problem of predicting what items a user intends to purchase, if any, given a click sequence performed during an activity session on the e-commerce website. The challenge was launched on November 15, 2014, and ran for seven months, attracting 850 teams from 49 countries which submitted a total of 5,437 solutions. The winners were determined based on the final ranking of the scores at the end of the challenge. However, in order to receive the monetary prize, the participants were required to submit, and have accepted, a paper detailing the applied algorithms, and attend the challenge's workshop. There were 22 submissions, and each submission was reviewed by at least two program committee members. The following table contains a summary of the 12 accepted papers and the corresponding score and rank in the final leaderboard.","PeriodicalId":324873,"journal":{"name":"Proceedings of the 2015 International ACM Recommender Systems Challenge","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115716461","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
Two-Stage Approach to Item Recommendation from User Sessions 从用户会话中推荐项目的两阶段方法
Proceedings of the 2015 International ACM Recommender Systems Challenge Pub Date : 2015-09-16 DOI: 10.1145/2813448.2813512
M. Volkovs
{"title":"Two-Stage Approach to Item Recommendation from User Sessions","authors":"M. Volkovs","doi":"10.1145/2813448.2813512","DOIUrl":"https://doi.org/10.1145/2813448.2813512","url":null,"abstract":"We present our solution to the 2015 RecSys Challenge [1]. This challenge was based on a large scale dataset of over 9.2 million user-item click sessions from an online e-commerce retailer. The goal was to use this data to predict which items (if any) were bought in the 2.3 million test sessions. Our solution to this problem was two-staged, we first predicted if a given session contained a buy event and then predicted which items were bought. Both stages were fully automated and used classifiers trained on large sets of extracted features. The prediction rules were further optimized to the target objective using a greedy procedure developed specifically for this problem. Our best submission, which was a blend of several different models, achieved a score of 60,265 and placed 4'th out of 567 teams. All approaches presented in this work are general and can be applied to any problem of this type.","PeriodicalId":324873,"journal":{"name":"Proceedings of the 2015 International ACM Recommender Systems Challenge","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128278162","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: 12
Probability-based Approach for Predicting E-commerce Consumer Behaviour Using Sparse Session Data 基于概率的稀疏会话数据预测电子商务消费者行为方法
Proceedings of the 2015 International ACM Recommender Systems Challenge Pub Date : 2015-09-16 DOI: 10.1145/2813448.2813514
Øyvind H. Myklatun, Thorstein K. Thorrud, H. Nguyen, H. Langseth, Anders Kofod-Petersen
{"title":"Probability-based Approach for Predicting E-commerce Consumer Behaviour Using Sparse Session Data","authors":"Øyvind H. Myklatun, Thorstein K. Thorrud, H. Nguyen, H. Langseth, Anders Kofod-Petersen","doi":"10.1145/2813448.2813514","DOIUrl":"https://doi.org/10.1145/2813448.2813514","url":null,"abstract":"This paper describes some of the key properties of the proposed solution for the RecSys 2015 Challenge from the team Tøyvind thørrud. Three contributions will be highlighted: i) Feature extraction, ii) Classifier design, and iii) Decision rules to optimize the prediction results towards the RecSys Challenge's score. We finished sixth out of more than 250 active teams in the competition.","PeriodicalId":324873,"journal":{"name":"Proceedings of the 2015 International ACM Recommender Systems Challenge","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114782822","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
Multi-Perspective Modeling for Click Event Prediction 点击事件预测的多视角建模
Proceedings of the 2015 International ACM Recommender Systems Challenge Pub Date : 2015-09-16 DOI: 10.1145/2813448.2813520
Tzu-Chun Lin, Xia Ning
{"title":"Multi-Perspective Modeling for Click Event Prediction","authors":"Tzu-Chun Lin, Xia Ning","doi":"10.1145/2813448.2813520","DOIUrl":"https://doi.org/10.1145/2813448.2813520","url":null,"abstract":"We present our solutions to the RecSys Challenge 2015. We propose a multi-perspective modeling scheme for click event prediction, which involves techniques from sophisticated feature engineering for both click sessions and clicked items, classification based on gradient boosting tree, semi-supervised learning that utilizes information from test data, multi-class classification for different categories of sessions and items, classifier-based feature fusion from multi-class classification and in the end classifier ensembles from multiple models. We demonstrate that our scheme is intuitive, flexible and powerful for the Challenge tasks. Our solution based on the scheme achieves a score of 49,517.2 in the Challenge.","PeriodicalId":324873,"journal":{"name":"Proceedings of the 2015 International ACM Recommender Systems Challenge","volume":"71 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123254848","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
RecSys Challenge 2015: ensemble learning with categorical features RecSys挑战2015:具有分类特征的集成学习
Proceedings of the 2015 International ACM Recommender Systems Challenge Pub Date : 2015-09-16 DOI: 10.1145/2813448.2813510
Peter Romov, Evgeny Sokolov
{"title":"RecSys Challenge 2015: ensemble learning with categorical features","authors":"Peter Romov, Evgeny Sokolov","doi":"10.1145/2813448.2813510","DOIUrl":"https://doi.org/10.1145/2813448.2813510","url":null,"abstract":"In this paper, we describe the winning approach for the RecSys Challenge 2015. Our key points are (1) two-stage classification, (2) massive usage of categorical features, (3) strong classifiers built by gradient boosting and (4) threshold optimization based directly on the competition score. We describe our approach and discuss how it can be used to build scalable personalization systems.","PeriodicalId":324873,"journal":{"name":"Proceedings of the 2015 International ACM Recommender Systems Challenge","volume":"257 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131852312","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: 42
In-House Solution for the RecSys Challenge 2015 2015年RecSys挑战的内部解决方案
Proceedings of the 2015 International ACM Recommender Systems Challenge Pub Date : 2015-09-16 DOI: 10.1145/2813448.2813519
Nadav Cohen, Adi Gerzi, David Ben-Shimon, Bracha Shapira, L. Rokach, Michael Friedmann
{"title":"In-House Solution for the RecSys Challenge 2015","authors":"Nadav Cohen, Adi Gerzi, David Ben-Shimon, Bracha Shapira, L. Rokach, Michael Friedmann","doi":"10.1145/2813448.2813519","DOIUrl":"https://doi.org/10.1145/2813448.2813519","url":null,"abstract":"RecSys Challenge 2015 is about predicting the items a user will buy in a given click session. We describe the in-house solution to the challenge as guided by the YOOCHOOSE team. The presented solution achieved 14th place in the challenge's final leaderboard with a score of 51,932 points, while the winner obtained 63,102 points. We suggest two simple and easy to reconstruct approaches for obtaining a prediction in each session. In the first approach we suggest one classifier to determine whether each item in the session will be bought. In the second approach we suggest a two level classification model in which the first level determines whether the session is going to end with a purchase or not, and if it ends with a purchase, the second level classification determines the items that are going to be purchased.","PeriodicalId":324873,"journal":{"name":"Proceedings of the 2015 International ACM Recommender Systems Challenge","volume":"115 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134363551","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
下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信