Proceedings of the Eleventh ACM Conference on Recommender Systems最新文献

Recommending a Sequence of Points of Interest to a Group of Users in a Mobile Context 在移动环境中向一组用户推荐一系列兴趣点

Proceedings of the Eleventh ACM Conference on Recommender Systems Pub Date : 2017-08-27 DOI: 10.1145/3109859.3109860

Daniel Herzog

引用次数: 17

RecSys Challenge 2017: Offline and Online Evaluation RecSys挑战2017:线下和线上评估

Proceedings of the Eleventh ACM Conference on Recommender Systems Pub Date : 2017-08-27 DOI: 10.1145/3109859.3109954

F. Abel, Yashar Deldjoo, Mehdi Elahi, Daniel Kohlsdorf

引用次数: 62

Citolytics: A Link-based Recommender System for Wikipedia Citolytics:一个基于链接的维基百科推荐系统

Proceedings of the Eleventh ACM Conference on Recommender Systems Pub Date : 2017-08-27 DOI: 10.1145/3109859.3109981

M. Schwarzer, Corinna Breitinger, M. Schubotz, Norman Meuschke, Bela Gipp

引用次数: 8

Deep Cross-Domain Fashion Recommendation 深度跨领域时尚推荐

Proceedings of the Eleventh ACM Conference on Recommender Systems Pub Date : 2017-08-27 DOI: 10.1145/3109859.3109861

Shatha Jaradat

{"title":"Deep Cross-Domain Fashion Recommendation","authors":"Shatha Jaradat","doi":"10.1145/3109859.3109861","DOIUrl":"https://doi.org/10.1145/3109859.3109861","url":null,"abstract":"With the increasing number of online shopping services, the number of users and the quantity of visual and textual information on the Internet, there is a pressing need for intelligent recommendation systems that analyze the user's behavior amongst multiple domains and help them to find the desirable information without the burden of search. However, there is little research that has been done on complex recommendation scenarios that involve knowledge transfer across multiple domains. This problem is especially challenging when the involved data sources are complex in terms of the limitations on the quantity and quality of data that can be crawled. The goal of this paper is studying the connection between visual and textual inputs for better analysis of a certain domain, and to examine the possibility of knowledge transfer from complex domains for the purpose of efficient recommendations. The methods employed to achieve this study include both design of architecture and algorithms using deep learning technologies to analyze the effect of deep pixel-wise semantic segmentation and text integration on the quality of recommendations. We plan to develop a practical testing environment in a fashion domain.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121171022","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: 40

A Novel Recommender System for Helping Marathoners to Achieve a New Personal-Best 一个新颖的推荐系统，帮助马拉松运动员达到一个新的个人最好

Proceedings of the Eleventh ACM Conference on Recommender Systems Pub Date : 2017-08-27 DOI: 10.1145/3109859.3109874

Barry Smyth, P. Cunningham

引用次数: 25

Defining and Supporting Narrative-driven Recommendation 定义和支持叙述驱动的推荐

Proceedings of the Eleventh ACM Conference on Recommender Systems Pub Date : 2017-08-27 DOI: 10.1145/3109859.3109893

Toine Bogers, M. Koolen

引用次数: 22

Improving the Trustworthiness of Recommendations in Collaborative Filtering under the Belief Function Framework 在信念函数框架下提高协同过滤推荐可信度

Proceedings of the Eleventh ACM Conference on Recommender Systems Pub Date : 2017-08-27 DOI: 10.1145/3109859.3109864

Raoua Abdelkhalek

{"title":"Improving the Trustworthiness of Recommendations in Collaborative Filtering under the Belief Function Framework","authors":"Raoua Abdelkhalek","doi":"10.1145/3109859.3109864","DOIUrl":"https://doi.org/10.1145/3109859.3109864","url":null,"abstract":"Collaborative Filtering (CF) consists of filtering data, predicting users' preferences and providing recommendations accordingly. Commonly, neighborhood-based CF methods predict the future ratings based on similar users (user-based) or similar items (item-based) to perform recommendations. However, the reliability of the information provided by these pieces of evidence as well as the final predictions cannot be fully trusted. Incorporating trust in the recommendation process can be argued to be an important challenge in Recommender Systems (RSs). To tackle these issues, we propose new CF approaches under the belief function framework. The final prediction is obtained by fusing evidences from similar items or similar users using Dempster's rule of combination. The prediction process of our evidential approaches is able to provide the users with a global overview of their possible preferences. This would lead to increase their confidence towards the system as well as their satisfaction. In this paper, we mainly highlight the benefits of incorporating uncertainty in CF approaches using the belief function theory. We present the preliminary results and also discuss our ongoing works, as well as the challenges in the future.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133317132","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

Modeling User Session and Intent with an Attention-based Encoder-Decoder Architecture 用基于注意力的编码器-解码器架构建模用户会话和意图

Proceedings of the Eleventh ACM Conference on Recommender Systems Pub Date : 2017-08-27 DOI: 10.1145/3109859.3109917

Pablo Loyola, Chen Liu, Yu Hirate

{"title":"Modeling User Session and Intent with an Attention-based Encoder-Decoder Architecture","authors":"Pablo Loyola, Chen Liu, Yu Hirate","doi":"10.1145/3109859.3109917","DOIUrl":"https://doi.org/10.1145/3109859.3109917","url":null,"abstract":"We propose an encoder-decoder neural architecture to model user session and intent using browsing and purchasing data from a large e-commerce company. We begin by identifying the source-target transition pairs between items within each session. Then, the set of source items are passed through an encoder, whose learned representation is used by the decoder to estimate the sequence of target items. Therefore, as this process is performed pair-wise, we hypothesize that the model could capture the transition regularities in a more fine grained way. Additionally, our model incorporates an attention mechanism to explicitly learn the more expressive portions of the sequences in order to improve performance. Besides modeling the user sessions, we also extended the original architecture by means of attaching a second decoder that is jointly trained to predict the purchasing intent of user in each session. With this, we want to explore to what extent the model can capture inter session dependencies. We performed an empirical study comparing against several baselines on a large real world dataset, showing that our approach is competitive in both item and intent prediction.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114943588","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: 66

Exploiting Socio-Economic Models for Lodging Recommendation in the Sharing Economy 基于社会经济模式的共享经济住宿推荐

Proceedings of the Eleventh ACM Conference on Recommender Systems Pub Date : 2017-08-27 DOI: 10.1145/3109859.3109910

Raúl Sánchez-Vázquez, Jordan Silva, Rodrygo L. T. Santos

{"title":"Exploiting Socio-Economic Models for Lodging Recommendation in the Sharing Economy","authors":"Raúl Sánchez-Vázquez, Jordan Silva, Rodrygo L. T. Santos","doi":"10.1145/3109859.3109910","DOIUrl":"https://doi.org/10.1145/3109859.3109910","url":null,"abstract":"Recent years have witnessed the emergence of sharing economy marketplaces, which enable users to share goods and services in a peer-to-peer fashion. A prominent example in the travel industry is Airbnb, which connects guests with hosts, allowing both to exchange cultural experiences in addition to the economic transaction. Nonetheless, Airbnb guest profiles are typically sparse, which limits the applicability of traditional lodging recommendation approaches. Inspired by recent socio-economic analyses of repurchase intent behavior on Airbnb, we propose a context-aware learning-to-rank approach for lodging recommendation, aimed to infer the user's perception of several dimensions involved in choosing which lodging to book. In particular, we devise features aimed to capture the user's price sensitivity as well as their perceived value of a particular lodging, the risk involved in choosing it rather than other available options, the authenticity of the cultural experience it could provide, and its overall perception by other users through word of mouth. Through a comprehensive evaluation using publicly available Airbnb data, we demonstrate the effectiveness of our proposed approach compared to a number of alternative recommendation baselines, including a simulation of Airbnb's own recommender.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124752474","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: 10

Practical Lessons from Developing a Large-Scale Recommender System at Zalando Zalando开发大规模推荐系统的实践经验

Proceedings of the Eleventh ACM Conference on Recommender Systems Pub Date : 2017-08-27 DOI: 10.1145/3109859.3109897

Antonino Freno

{"title":"Practical Lessons from Developing a Large-Scale Recommender System at Zalando","authors":"Antonino Freno","doi":"10.1145/3109859.3109897","DOIUrl":"https://doi.org/10.1145/3109859.3109897","url":null,"abstract":"Developing a real-world recommender system, i.e. for use in large-scale online retail, poses a number of different challenges. Interestingly, only a small part of these challenges are of algorithmic nature, such as how to select the most accurate model for a given use case. Instead, most technical problems usually arise from operational constraints, such as: adaptation to novel use cases; cost and complexity of system maintenance; capability of reusing pre-existing signal and integrating heterogeneous data sources. In this paper, we describe the system we developed in order to address those constraints at Zalando, which is one of the most popular online fashion retailers in Europe. In particular, we explain how moving from a collaborative filtering approach to a learning-to-rank model helped us to effectively tackle the challenges mentioned above, while improving at the same time the quality of our recommendations. A fairly detailed description of our software architecture is provided, along with an overview of the algorithmic approach. On the other hand, we present some of the offline and online experiments that we ran in order to validate our models.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130459959","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: 22