Proceedings of the Eleventh ACM Conference on Recommender Systems最新文献_第10页

PathRec: Visual Analysis of Travel Route Recommendations PathRec:旅游路线推荐的可视化分析

Proceedings of the Eleventh ACM Conference on Recommender Systems Pub Date : 2017-07-06 DOI: 10.1145/3109859.3109983

Dawei Chen, Dongwoo Kim, Lexing Xie, Minjeong Shin, A. Menon, Cheng Soon Ong, Iman Avazpour, J. Grundy

引用次数: 11

Getting Deep Recommenders Fit: Bloom Embeddings for Sparse Binary Input/Output Networks 获得深度推荐器拟合:稀疏二进制输入/输出网络的Bloom嵌入

Proceedings of the Eleventh ACM Conference on Recommender Systems Pub Date : 2017-06-13 DOI: 10.1145/3109859.3109876

J. Serrà, Alexandros Karatzoglou

{"title":"Getting Deep Recommenders Fit: Bloom Embeddings for Sparse Binary Input/Output Networks","authors":"J. Serrà, Alexandros Karatzoglou","doi":"10.1145/3109859.3109876","DOIUrl":"https://doi.org/10.1145/3109859.3109876","url":null,"abstract":"Recommendation algorithms that incorporate techniques from deep learning are becoming increasingly popular. Due to the structure of the data coming from recommendation domains (i.e., one-hot-encoded vectors of item preferences), these algorithms tend to have large input and output dimensionalities that dominate their overall size. This makes them difficult to train, due to the limited memory of graphical processing units, and difficult to deploy on mobile devices with limited hardware. To address these difficulties, we propose Bloom embeddings, a compression technique that can be applied to the input and output of neural network models dealing with sparse high-dimensional binary-coded instances. Bloom embeddings are computationally efficient, and do not seriously compromise the accuracy of the model up to 1/5 compression ratios. In some cases, they even improve over the original accuracy, with relative increases up to 12%. We evaluate Bloom embeddings on 7 data sets and compare it against 4 alternative methods, obtaining favorable results. We also discuss a number of further advantages of Bloom embeddings, such as 'on-the-fly' constant-time operation, zero or marginal space requirements, training time speedups, or the fact that they do not require any change to the core model architecture or training configuration.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"10 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115336401","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: 52

Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks 基于会话的个性化推荐与递归神经网络

Proceedings of the Eleventh ACM Conference on Recommender Systems Pub Date : 2017-06-13 DOI: 10.1145/3109859.3109896

Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, P. Cremonesi

引用次数: 533

The Magic Barrier Revisited: Accessing Natural Limitations of Recommender Assessment 重新审视魔法障碍:访问推荐人评估的自然限制

Proceedings of the Eleventh ACM Conference on Recommender Systems Pub Date : 2017-04-19 DOI: 10.1145/3109859.3109898

Kevin Jasberg, Sergej Sizov

{"title":"The Magic Barrier Revisited: Accessing Natural Limitations of Recommender Assessment","authors":"Kevin Jasberg, Sergej Sizov","doi":"10.1145/3109859.3109898","DOIUrl":"https://doi.org/10.1145/3109859.3109898","url":null,"abstract":"Recommender systems nowadays have many applications and are of great economic benefit. Hence, it is imperative for success-oriented companies to compare various of such systems and select the better one for their purposes. To this end, various metrics of predictive accuracy are commonly used, such as the Root Mean Square Error (RMSE), or precision and recall. All these metrics more or less measure how well a recommender system can predict human behaviour. Unfortunately, human behaviour is always associated with some degree of uncertainty, making the evaluation difficult, since it is not clear whether a deviation is system-induced or just originates from the natural variability of human decision making. At this point, some authors speculated that we may be reaching some Magic Barrier where this variability prevents us from getting much more accurate [12, 13, 24]. In this article, we will extend the existing theory of the Magic Barrier [24] into a new probabilistic but a yet pragmatic model. In particular, we will use methods from metrology and physics to develop easy-to-handle quantities for computation to describe the Magic Barrier for different accuracy metrics and provide suggestions for common application. This discussion is substantiated by comprehensive experiments with real users and large-scale simulations on a high-performance cluster.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122190013","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: 15

TransNets: Learning to Transform for Recommendation TransNets:学习转换为推荐

Proceedings of the Eleventh ACM Conference on Recommender Systems Pub Date : 2017-04-07 DOI: 10.1145/3109859.3109878

R. Catherine, William W. Cohen

{"title":"TransNets: Learning to Transform for Recommendation","authors":"R. Catherine, William W. Cohen","doi":"10.1145/3109859.3109878","DOIUrl":"https://doi.org/10.1145/3109859.3109878","url":null,"abstract":"Recently, deep learning methods have been shown to improve the performance of recommender systems over traditional methods, especially when review text is available. For example, a recent model, DeepCoNN, uses neural nets to learn one latent representation for the text of all reviews written by a target user, and a second latent representation for the text of all reviews for a target item, and then combines these latent representations to obtain state-of-the-art performance on recommendation tasks. We show that (unsurprisingly) much of the predictive value of review text comes from reviews of the target user for the target item. We then introduce a way in which this information can be used in recommendation, even when the target user's review for the target item is not available. Our model, called TransNets, extends the DeepCoNN model by introducing an additional latent layer representing the target user-target item pair. We then regularize this layer, at training time, to be similar to another latent representation of the target user's review of the target item. We show that TransNets and extensions of it improve substantially over the previous state-of-the-art.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130357091","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: 240

Proceedings of the Eleventh ACM Conference on Recommender Systems 第十一届ACM推荐系统会议论文集

Proceedings of the Eleventh ACM Conference on Recommender Systems Pub Date : 1900-01-01 DOI: 10.1145/3109859

引用次数: 16