Proceedings of the 13th ACM Conference on Recommender Systems最新文献_第2页

Attribute-based evaluation for recommender systems: incorporating user and item attributes in evaluation metrics 基于属性的推荐系统评价:在评价指标中纳入用户和项目属性

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3347049

Pablo Sánchez, Alejandro Bellogín

{"title":"Attribute-based evaluation for recommender systems: incorporating user and item attributes in evaluation metrics","authors":"Pablo Sánchez, Alejandro Bellogín","doi":"10.1145/3298689.3347049","DOIUrl":"https://doi.org/10.1145/3298689.3347049","url":null,"abstract":"Research in Recommender Systems evaluation remains critical to study the efficiency of developed algorithms. Even if different aspects have been addressed and some of its shortcomings - such as biases, robustness, or cold start - have been analyzed and solutions or guidelines have been proposed, there are still some gaps that need to be further investigated. At the same time, the increasing amount of data collected by most recommender systems allows to gather valuable information from users and items which is being neglected by classical offline evaluation metrics. In this work, we integrate such information into the evaluation process in two complementary ways: on the one hand, we aggregate any evaluation metric according to the groups defined by the user attributes, and, on the other hand, we exploit item attributes to consider some recommended items as surrogates of those interacted by the user, with a proper penalization. Our results evidence that this novel evaluation methodology allows to capture different nuances of the algorithms performance, inherent biases in the data, and even fairness of the recommendations.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"21 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114102101","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: 8

Data mining for item recommendation in MOBA games MOBA游戏中道具推荐的数据挖掘

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3346986

Vladimir Araujo, Felipe Rios, Denis Parra

引用次数: 17

CB2CF CB2CF

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3347038

Oren Barkan, Noam Koenigstein, E. Yogev, O. Katz

引用次数: 2

Recommender systems for contextually-aware, versioned items 上下文感知、版本化项目的推荐系统

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3346955

Yayu Zhou

引用次数: 0

Music cold-start and long-tail recommendation: bias in deep representations 音乐冷启动和长尾推荐:深度表征中的偏差

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3347052

Andrés Ferraro

{"title":"Music cold-start and long-tail recommendation: bias in deep representations","authors":"Andrés Ferraro","doi":"10.1145/3298689.3347052","DOIUrl":"https://doi.org/10.1145/3298689.3347052","url":null,"abstract":"Recent advances in deep learning have yielded new approaches for music recommendation in the long tail. The new approaches are based on data related to the music content (i.e. the audio signal) and context (i.e. other textual information), from which it automatically obtains a representation in a latent space that is used to generate the recommendations. The authors of these new approaches have shown improved accuracies, thus becoming the new state-of-the-art for music recommendation in the long tail. One of the drawbacks of these methods is that it is not possible to understand how the recommendations are generated and what the different dimensions of the underlying models represent. The goal of this thesis is to evaluate these models to understand how good are the results from the user perspective and how successful the models are to recommend new artists or less-popular music genres and styles (i.e. the long tail). For example, if a model predicts the latent representation from the audio but a given genre is not well represented in the collection, it is not probable that the songs of this genre are going to be recommended. First, we will focus on defining a measure that could be used to assess how successful a model is recommending new artists or less-popular genres. Then, the state-of-the-art methods will be evaluated offline to understand how they perform under different circumstances and new methods will be proposed. Later, using an online evaluation it will be possible to understand how these recommendations are perceived by the users. Increasingly, algorithms are responsible for the music that we consume, understanding their behavior is fundamental to make sure they give the opportunity to new artists and music styles. This work will contribute in this direction, making it possible to give better recommendations for the users.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131558655","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: 28

Exploiting contextual information for recommender systems oriented to tourism 为旅游推荐系统开发上下文信息

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3347062

Pablo Sánchez

引用次数: 8

Personalized diffusions for top-n recommendation 个性化扩散的顶级推荐

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3346985

A. Nikolakopoulos, Dimitris Berberidis, G. Karypis, G. Giannakis

引用次数: 16

When actions speak louder than clicks: a combined model of purchase probability and long-term customer satisfaction 当行动比点击更响亮:购买概率和长期客户满意度的组合模型

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3347044

G. Lavee, Noam Koenigstein, Oren Barkan

{"title":"When actions speak louder than clicks: a combined model of purchase probability and long-term customer satisfaction","authors":"G. Lavee, Noam Koenigstein, Oren Barkan","doi":"10.1145/3298689.3347044","DOIUrl":"https://doi.org/10.1145/3298689.3347044","url":null,"abstract":"Maximizing sales and revenue is an important goal of online commercial retailers. Recommender systems are designed to maximize users' click or purchase probability, but often disregard users' eventual satisfaction with purchased items. As result, such systems promote items with high appeal at the selling stage (e.g. an eyecatching presentation) over items that would yield more satisfaction to users in the long run. This work presents a novel unified model that considers both goals and can be tuned to balance between them according to the needs of the business scenario. We propose a multi-task probabilistic matrix factorization model with a dual task objective: predicting binary purchase/no purchase variables combined with predicting continuous satisfaction scores. Model parameters are optimized using Variational Bayes which allows learning a posterior distribution over model parameters. This model allows making predictions that balance the two goals of maximizing the probability for an immediate purchase and maximizing user satisfaction and engagement down the line. These goals lie at the heart of most commercial recommendation scenarios and enabling their balance has the potential to improve value for millions of users worldwide. Finally, we present experimental evaluation on different types of consumer retail datasets that demonstrate the benefits of the model over popular baselines on a number of well-known ranking metrics.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116358605","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

DualDiv: diversifying items and explanation styles in explainable hybrid recommendation DualDiv:在可解释的混合推荐中多样化的项目和解释风格

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3347063

Kosetsu Tsukuda, Masataka Goto

引用次数: 11

Recommender system for developing new preferences and goals 开发新的偏好和目标的推荐系统

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3347054

Yu Liang

引用次数: 16