Proceedings of the 13th ACM Conference on Recommender Systems最新文献

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Third workshop on recommendation in complex scenarios (ComplexRec 2019) 第三届复杂场景推荐研讨会(ComplexRec 2019)
Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3346948
M. Koolen, Toine Bogers, B. Mobasher, A. Tuzhilin
{"title":"Third workshop on recommendation in complex scenarios (ComplexRec 2019)","authors":"M. Koolen, Toine Bogers, B. Mobasher, A. Tuzhilin","doi":"10.1145/3298689.3346948","DOIUrl":"https://doi.org/10.1145/3298689.3346948","url":null,"abstract":"Over the past decade, recommendation algorithms for ratings prediction and item ranking have steadily matured. However, these state-of-the-art algorithms are typically applied in relatively straightforward and static scenarios: given information about a user's past item preferences in isolation, can we predict whether they will like a new item or rank all unseen items based on predicted interest? In reality, recommendation is often a more complex problem: the evaluation of a list of recommended items never takes place in a vacuum, and it is often a single step in the user's more complex background task or need. The goal of the ComplexRec 2019 workshop is to offer an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size-fits-all solution.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"22 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":"116909650","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
Fairness and discrimination in recommendation and retrieval 推荐与检索中的公平与歧视
Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3346964
{"title":"Fairness and discrimination in recommendation and retrieval","authors":"","doi":"10.1145/3298689.3346964","DOIUrl":"https://doi.org/10.1145/3298689.3346964","url":null,"abstract":"Fairness and related concerns have become of increasing importance in a variety of AI and machine learning contexts. They are also highly relevant to recommender systems and related problems such as information retrieval, as evidenced by the growing literature in RecSys, FAT*, SIGIR, and special sessions such as the FATREC and FACTS-IR workshops and the Fairness track at TREC 2019; however, translating algorithmic fairness constructs from classification, scoring, and even many ranking settings into recommendation and other information access scenarios is not a straightforward task. This tutorial will help orient RecSys researchers to algorithmic fairness, understand how concepts do and do not translate from other settings, and provide an introduction to the growing literature on this topic.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"73 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":"127211931","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: 31
Ghosting: contextualized inline query completion in large scale retail search 重影:大规模零售搜索中上下文化的内联查询完成
Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3346995
Lakshmi Ramachandran, Uma Murthy
{"title":"Ghosting: contextualized inline query completion in large scale retail search","authors":"Lakshmi Ramachandran, Uma Murthy","doi":"10.1145/3298689.3346995","DOIUrl":"https://doi.org/10.1145/3298689.3346995","url":null,"abstract":"Query auto-completion presents a ranked list of queries as suggestions for a user-entered prefix. Ghosting is the process of auto-completing a search recommendation by highlighting the suggested text inline within the search box. We propose the use of a behavior-based recommendation model along with customer search context to ghost on high-confidence queries. We tested ghosting on a retail production system, on over 140 million search sessions. We found that session-context based ghosting significantly increased the acceptance of offered suggestions by 6.18%, reduced misspellings among searches by 4.42%, and improved net sales by 0.14%.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"17 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":"114893010","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
User's activity driven short-term context inference 用户活动驱动短期上下文推断
Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3346950
Miroslav Rac
{"title":"User's activity driven short-term context inference","authors":"Miroslav Rac","doi":"10.1145/3298689.3346950","DOIUrl":"https://doi.org/10.1145/3298689.3346950","url":null,"abstract":"Customer decision making process is not invariant. Actual circumstances have a great influence on user's preference adjustments, therefore an absence of incorporating contextual information leads to sub-optimal prediction performance. A popular approach in recommender systems is to treat a context as a set of identifiable and observable attributes while assuming their full separability from an activity. In contrast, we believe that the context emerges from the activity and its change can be perceived and possibly predicted by using mined patterns of its evolution on multiple levels, starting at individual sessions. This paper presents concepts, ideas and motivation for our PhD research project.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"36 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":"114898048","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
Asymmetric Bayesian personalized ranking for one-class collaborative filtering 一类协同过滤的非对称贝叶斯个性化排序
Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3347051
Sha Ouyang, Lin Li, Weike Pan, Zhong Ming
{"title":"Asymmetric Bayesian personalized ranking for one-class collaborative filtering","authors":"Sha Ouyang, Lin Li, Weike Pan, Zhong Ming","doi":"10.1145/3298689.3347051","DOIUrl":"https://doi.org/10.1145/3298689.3347051","url":null,"abstract":"In this paper, we propose a novel preference assumption for modeling users' one-class feedback such as \"thumb up\" in an important recommendation problem called one-class collaborative filtering (OCCF). Specifically, we address a fundamental limitation of a recent symmetric pairwise preference assumption and propose a novel and first asymmetric one, which is able to make the preferences of different users more comparable. With the proposed asymmetric pairwise preference assumption, we further design a novel recommendation algorithm called asymmetric Bayesian personalized ranking (ABPR). Extensive empirical studies on two large and public datasets show that our ABPR performs significantly better than several state-of-the-art recommendation methods with either pointwise preference assumption or pairwise preference assumption.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"49 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":"116113896","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
PyRecGym
Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3346981
Bichen Shi, Makbule Gülçin Özsoy, Neil Hurley, Barry Smyth, E. Tragos, James Geraci, A. Lawlor
{"title":"PyRecGym","authors":"Bichen Shi, Makbule Gülçin Özsoy, Neil Hurley, Barry Smyth, E. Tragos, James Geraci, A. Lawlor","doi":"10.1145/3298689.3346981","DOIUrl":"https://doi.org/10.1145/3298689.3346981","url":null,"abstract":"Recommender systems (RS) share many features and objectives with reinforcement learning (RL) systems. The former aim to maximise user satisfaction by recommending the right items to the right users at the right time, the latter maximise future rewards by selecting state-changing actions in some environment. The concept of an RL gym has become increasingly important when it comes to supporting the development of RL models. A gym provides a simulation environment in which to test and develop RL agents, providing a state model, actions, rewards/penalties etc. In this paper we describe and demonstrate the PyRecGym gym, which is specifically designed for the needs of recommender systems research, by supporting standard test datasets (MovieLens, Yelp etc.), common input types (text, numeric etc.), and thereby offering researchers a reproducible research environment to accelerate experimentation and development of RL in RS.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"86 1 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":"121931925","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: 25
Traversing semantically annotated queries for task-oriented query recommendation 为面向任务的查询推荐遍历带有语义注释的查询
Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3346994
A. Câmara, Rodrygo L. T. Santos
{"title":"Traversing semantically annotated queries for task-oriented query recommendation","authors":"A. Câmara, Rodrygo L. T. Santos","doi":"10.1145/3298689.3346994","DOIUrl":"https://doi.org/10.1145/3298689.3346994","url":null,"abstract":"As search systems gradually turn into intelligent personal assistants, users increasingly resort to a search engine to accomplish a complex task, such as planning a trip, renting an apartment, or investing in stocks. A key challenge for the search engine is to understand the user's underlying task given a sample query like \"tickets to panama\", \"studios in los angeles\", or \"spotify stocks\", and to suggest other queries to help the user complete the task. In this paper, we investigate several strategies for query recommendation by traversing a semantically annotated query log using a mixture of explicit and latent representations of entire queries and of query segments. Our results demonstrate the effectiveness of these strategies in terms of utility and diversity, as well as their complementarity, with significant improvements compared to state-of-the-art query recommendation baselines adapted for this task.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"1 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":"129850455","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
Performance comparison of neural and non-neural approaches to session-based recommendation 基于会话的推荐中神经和非神经方法的性能比较
Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3347041
Malte Ludewig, Noemi Mauro, Sara Latifi, D. Jannach
{"title":"Performance comparison of neural and non-neural approaches to session-based recommendation","authors":"Malte Ludewig, Noemi Mauro, Sara Latifi, D. Jannach","doi":"10.1145/3298689.3347041","DOIUrl":"https://doi.org/10.1145/3298689.3347041","url":null,"abstract":"The benefits of neural approaches are undisputed in many application areas. However, today's research practice in applied machine learning---where researchers often use a variety of baselines, datasets, and evaluation procedures---can make it difficult to understand how much progress is actually achieved through novel technical approaches. In this work, we focus on the fast-developing area of session-based recommendation and aim to contribute to a better understanding of what represents the state-of-the-art. To that purpose, we have conducted an extensive set of experiments, using a variety of datasets, in which we benchmarked four neural approaches that were published in the last three years against each other and against a set of simpler baseline techniques, e.g., based on nearest neighbors. The evaluation of the algorithms under the exact same conditions revealed that the benefits of applying today's neural approaches to session-based recommendations are still limited. In the majority of the cases, and in particular when precision and recall are used, it turned out that simple techniques in most cases outperform recent neural approaches. Our findings therefore point to certain major limitations of today's research practice. By sharing our evaluation framework publicly, we hope that some of these limitations can be overcome in the future.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"1 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":"128406240","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: 86
A generative model for review-based recommendations 基于评论的推荐生成模型
Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3347061
Oren Sar Shalom, Guy Uziel, Amir Kantor
{"title":"A generative model for review-based recommendations","authors":"Oren Sar Shalom, Guy Uziel, Amir Kantor","doi":"10.1145/3298689.3347061","DOIUrl":"https://doi.org/10.1145/3298689.3347061","url":null,"abstract":"User generated reviews is a highly informative source of information, that has recently gained lots of attention in the recommender systems community. In this work we propose a generative latent variable model that explains both observed ratings and textual reviews. This latent variable model allows to combine any traditional collaborative filtering method, together with any deep learning architecture for text processing. Experimental results on four benchmark datasets demonstrate its superiority comparing to all baseline recommender systems. Furthermore, a running time analysis shows that this approach is in order of magnitude faster that relevant baselines. Moreover, underlying our solution there is a general framework that may be further explored.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"32 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":"128757092","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: 11
HybridSVD
Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3347055
Evgeny Frolov, I. Oseledets
{"title":"HybridSVD","authors":"Evgeny Frolov, I. Oseledets","doi":"10.1145/3298689.3347055","DOIUrl":"https://doi.org/10.1145/3298689.3347055","url":null,"abstract":"We propose a new hybrid algorithm that allows incorporating both user and item side information within the standard collaborative filtering technique. One of its key features is that it naturally extends a simple PureSVD approach and inherits its unique advantages, such as highly efficient Lanczos-based optimization procedure, simplified hyper-parameter tuning and a quick folding-in computation for generating recommendations instantly even in highly dynamic online environments. The algorithm utilizes a generalized formulation of the singular value decomposition, which adds flexibility to the solution and allows imposing the desired structure on its latent space. Conveniently, the resulting model also admits an efficient and straightforward solution for the cold start scenario. We evaluate our approach on a diverse set of datasets and show its superiority over similar classes of hybrid models.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"14 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":"121225880","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
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