Proceedings of the 9th ACM Conference on Recommender Systems最新文献

Latent Context-Aware Recommender Systems 潜在的上下文感知推荐系统

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2796546

Moshe Unger

{"title":"Latent Context-Aware Recommender Systems","authors":"Moshe Unger","doi":"10.1145/2792838.2796546","DOIUrl":"https://doi.org/10.1145/2792838.2796546","url":null,"abstract":"The emergence of smart mobile devices has given rise to the development of context-aware systems that utilize sensors to collect available data about users. This data, in turn, is used in order to improve various services for the user. The development of such applications is inherently complex, since these applications adapt to changing context information, such as: physical context, computational context, and user tasks. Context information is gathered from a variety of sources that differ in the quality of information they produce and that are often failure-prone. Our study is part of a growing research effort that examines how data collected from mobile devices can be utilized to infer users' behavior and environment. We propose novel approaches that use a rich set of mobile sensors in order to infer unexplored users' contexts in personal models. We also suggest utilizing these high dimensional sensors, which represent users' context for a CARS (context-aware recommender system). For this purpose, we suggest several methods for reducing the dimensionality space by extracting latent contexts from data collected by mobile device sensors. Latent contexts are hidden context patterns, modeled as numeric vectors that are learned for each user automatically, by utilizing unsupervised deep learning techniques on the collected data. We also describe a novel latent context recommendation technique that uses latent contexts and improves the accuracy of state-of-the-art CARS. A preliminary analysis reveals encouraging insights regarding the feasibility of latent contexts and their utilization for context-aware recommendation systems.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"13 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":"116031677","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: 23

A Study of Priors for Relevance-Based Language Modelling of Recommender Systems 基于关联的推荐系统语言建模的先验研究

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2799677

Daniel Valcarce, Javier Parapar, Álvaro Barreiro

引用次数: 11

Health-aware Food Recommender System 健康食品推荐系统

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2796554

Mouzhi Ge, F. Ricci, David Massimo

引用次数: 114

Large-Scale Real-Time Product Recommendation at Criteo Criteo的大规模实时产品推荐

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2799498

Romain Lerallut, Diane Gasselin, Nicolas Le Roux

引用次数: 9

Factorization Machines for Hybrid Recommendation Systems Based on Behavioral, Product, and Customer Data 基于行为、产品和客户数据的混合推荐系统的分解机

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2796542

S. Geuens

引用次数: 5

User Churn Migration Analysis with DEDICOM 使用DEDICOM分析用户流失迁移

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2799680

R. Sifa, C. Ojeda, C. Bauckhage

引用次数: 18

Blockbusters and Wallflowers: Accurate, Diverse, and Scalable Recommendations with Random Walks 大片和壁花:精确、多样和可扩展的随机漫步推荐

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2800180

F. Christoffel, B. Paudel, Chris Newell, A. Bernstein

引用次数: 63

Latent Trajectory Modeling: A Light and Efficient Way to Introduce Time in Recommender Systems 潜在轨迹建模:在推荐系统中引入时间的一种简单有效的方法

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2799676

Élie Guàrdia-Sebaoun, Vincent Guigue, P. Gallinari

引用次数: 26

Learning Distributed Representations from Reviews for Collaborative Filtering 从评论中学习分布式表示用于协同过滤

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2800192

Amjad Almahairi, Kyle Kastner, Kyunghyun Cho, Aaron C. Courville

引用次数: 110

PushTrust: An Efficient Recommendation Algorithm by Leveraging Trust and Distrust Relations PushTrust:利用信任和不信任关系的高效推荐算法

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2800198

R. Forsati, Iman Barjasteh, Farzan Masrour, A. Esfahanian, H. Radha

{"title":"PushTrust: An Efficient Recommendation Algorithm by Leveraging Trust and Distrust Relations","authors":"R. Forsati, Iman Barjasteh, Farzan Masrour, A. Esfahanian, H. Radha","doi":"10.1145/2792838.2800198","DOIUrl":"https://doi.org/10.1145/2792838.2800198","url":null,"abstract":"The significance of social-enhanced recommender systems is increasing, along with its practicality, as online reviews, ratings, friendship links, and follower relationships are increasingly becoming available. In recent years, there has been an upsurge of interest in exploiting social information, such as trust and distrust relations in recommendation algorithms. The goal is to improve the quality of suggestions and mitigate the data sparsity and the cold-start users problems in existing systems. In this paper, we introduce a general collaborative social ranking model to rank the latent features of users extracted from rating data based on the social context of users. In contrast to existing social regularization methods, the proposed framework is able to simultaneously leverage trust, distrust, and neutral relations, and has a linear dependency on the social network size. By integrating the ranking based social regularization idea into the matrix factorization algorithm, we propose a novel recommendation algorithm, dubbed PushTrust. Our experiments on the Epinions dataset demonstrate that collaboratively ranking the latent features of users by exploiting trust and distrust relations leads to a substantial increase in performance, and to effectively deal with cold-start users problem.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"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":"129080997","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: 43