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

{"title":"\"Just play something awesome\": the personalization powering voice interactions at Pandora","authors":"V. Ostuni","doi":"10.1145/3298689.3347064","DOIUrl":"https://doi.org/10.1145/3298689.3347064","url":null,"abstract":"The adoption of voice-enabled devices has seen an explosive growth in the last few years and music consumption is among the most popular use cases. Music personalization and recommendation plays a major role at Pandora in providing a delightful listening experience for millions of users daily. In turn, providing the same perfectly tailored listening experience through these novel voice interfaces brings new interesting challenges and exciting opportunities. In this talk we will describe how we apply personalization and recommendation techniques in three common voice scenarios which can be defined in terms of request types: known-item, thematic and broad open-ended. Known-item search requests are the most common scenario where users have a well defined and clear intent which is looking for a specific item in the catalog or their personal collection. A voice interface makes the task natural and easy to accomplish since the user is not required to type on a small keyboard. Solving for this specific task involves performing an entity search against a large music catalog and personal user collection. This can be very challenging due to imperfect voice utterance transcriptions, unconventional entity names and the numerous combinations of ways a user can ask for music entities. We employ personalization algorithms for entity disambiguation which can be caused by the presence of homonyms, homographs and homophones terms in the catalog. Another common voice use case is to ask for music regarding a specific theme or context such as a genre, an activity, a mood, an occasion or any combination of those. This scenario differs sharply from the known-item case in that multiple results might, based on user varying contexts, be relevant rather than a single clearly relevant one. For example, a rap music fan would not enjoy a country workout playlist when asking for \"music for working out\" but may like a hip hop one. This problem can be quite complex to solve as it involves different areas such as voice spoken language understanding, content tagging and personalization. We will describe how we use deep learning slot filling techniques and query classification to interpret the user intent and identify the main concepts in the query. After that, we will discuss some of the content tagging work we have done to classify music according to these voice specific themes. Lastly, we will touch upon how we use recommendation techniques to deliver personalized and unique results to each individual and describe the challenge of balancing the delicate trade-off between query relevance and personalization. The third category of voice queries we will describe are broad or open-ended requests. Voice users often skip the hard work of thinking about what they actually want to hear and command: \"just play something awesome\". A music service should still meet these expectations instead of interpreting those commands as literal requests. We discuss exploit and explore trade-offs made ","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"84 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":"121208163","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}

{"title":"RecTour 2019: workshop on recommenders in tourism","authors":"J. Neidhardt, W. Wörndl, T. Kuflik, M. Zanker, Catalin-Mihai Barbu","doi":"10.1145/3298689.3346969","DOIUrl":"https://doi.org/10.1145/3298689.3346969","url":null,"abstract":"The Workshop on Recommenders in Tourism (RecTour) 2019, which is held in conjunction with the 13th ACM Conference on Recommender Systems (RecSys), addresses specific challenges for recommender systems in the tourism domain. In this overview paper, we summarize our motivations to organize the RecTour workshop and present the main topics of the submissions that we received. The topics of this year's workshop include context-aware recommendations, group recommender systems, hotel recommendations, destination characterization, next-POI recommendation, user interaction and experience, preference elicitation, user modeling and application of machine learning algorithms in the context of tourism recommender systems.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"31 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":"126731621","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}

{"title":"Adversarial tensor factorization for context-aware recommendation","authors":"Huiyuan Chen, Jing Li","doi":"10.1145/3298689.3346987","DOIUrl":"https://doi.org/10.1145/3298689.3346987","url":null,"abstract":"Contextual factors such as time, location, or tag, can affect user preferences for a particular item. Context-aware recommendations are thus critical to improve both quality and explainability of recommender systems, compared to traditional recommendations that are solely based on user-item interactions. Tensor factorization machines have achieved the state-of-the-art performance due to their capability of integrating users, items, and contextual factors in one unify way. However, few work has focused on the robustness of a context-aware recommender system. Improving the robustness of a tensor-based model is challenging due to the sparsity of the observed tensor and the multi-linear nature of tensor factorization. In this paper, we propose ATF, a model that combines tensor factorization and adversarial learning for context-aware recommendations. Doing so allows us to reap the benefits of tensor factorization, while enhancing the robustness of a recommender model, and thus improves its eventual performance. Empirical studies on two real-world datasets show that the proposed method outperforms standard tensor-based methods.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"24 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":"116669930","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}

{"title":"Adversarial attacks on an oblivious recommender","authors":"Konstantina Christakopoulou, A. Banerjee","doi":"10.1145/3298689.3347031","DOIUrl":"https://doi.org/10.1145/3298689.3347031","url":null,"abstract":"Can machine learning models be easily fooled? Despite the recent surge of interest in learned adversarial attacks in other domains, in the context of recommendation systems this question has mainly been answered using hand-engineered fake user profiles. This paper attempts to reduce this gap. We provide a formulation for learning to attack a recommender as a repeated general-sum game between two players, i.e., an adversary and a recommender oblivious to the adversary's existence. We consider the challenging case of poisoning attacks, which focus on the training phase of the recommender model. We generate adversarial user profiles targeting subsets of users or items, or generally the top-K recommendation quality. Moreover, we ensure that the adversarial user profiles remain unnoticeable by preserving proximity of the real user rating/interaction distribution to the adversarial fake user distribution. To cope with the challenge of the adversary not having access to the gradient of the recommender's objective with respect to the fake user profiles, we provide a non-trivial algorithm building upon zero-order optimization techniques. We offer a wide range of experiments, instantiating the proposed method for the case of the classic popular approach of a low-rank recommender, and illustrating the extent of the recommender's vulnerability to a variety of adversarial intents. These results can serve as a motivating point for more research into recommender defense strategies against machine learned attacks.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"38 4 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":"116789344","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}

{"title":"Recommendation systems compliant with legal and editorial policies: the BBC+ app journey","authors":"Maria Panteli","doi":"10.1145/3298689.3346961","DOIUrl":"https://doi.org/10.1145/3298689.3346961","url":null,"abstract":"The BBC produces thousands of pieces of content every day and numerous BBC products deliver this content to millions of users. For many years the content has been manually curated (this is evident in the selection of stories on the front page of the BBC News website and app for example). To support content creation and curation, a set of editorial guidelines have been developed to build quality and trust in the BBC. As personalisation becomes more important for audience engagement, we have been exploring how algorithmically-driven recommendations could be integrated in our products. In this talk we describe how we developed recommendation systems for the BBC+ app that comply with legal and editorial policies and promote the values of the organisation. We also discuss the challenges we face moving forward, extending the use of recommendation systems for a public service media organisation like the BBC. The BBC+ app is the first product to host in-house recommendations in a fully algorithmically-driven application. The app surfaces short video clips and is targeted at younger audiences. The first challenge we dealt with was content metadata. Content metadata are created for different purposes and managed by different teams across the organisation making it difficult to have reliable and consistent information. Metadata enrichment strategies have been applied to identify content that is considered to be editorially sensitive, such as political content, current legal cases, archived news, commercial content, and content unsuitable for an under 16 audience. Metadata enrichment is also applied to identify content that due care has not been taken such as poor titles, and spelling and grammar mistakes. The first versions of recommendation algorithms exclude all editorially risky content from the recommendations, the most serious of which is avoiding contempt of court. In other cases we exclude content that could undermine our quality and trustworthiness. The General Data Protection Regulation (GDPR) that recently came into effect had strong implications on the design of our system architecture, the choice of the recommendation models, and the implementation of specific product features. For example, the user should be able to delete their data or switch off personalisation at any time. Our system architecture should allow us to trace down and delete all data from that user and switch to non-personalised content. The recommendations should also be explainable and this led us to sometimes choosing a simpler model so that it is possible to more easily explain why a user was recommended a particular type of content. Specific product features were also added to enhance transparency and explainability. For example, the user could view their history of watched items, delete any item, and get an explanation of why a piece of content was recommended to them. At the BBC we aim to not only entertain our audiences but also to inform and educate. These BBC values are","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"74 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":"115630780","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}

{"title":"Rude awakenings from behaviourist dreams. Methodological integrity and the GDPR","authors":"M. Hildebrandt","doi":"10.1145/3298689.3346980","DOIUrl":"https://doi.org/10.1145/3298689.3346980","url":null,"abstract":"Recommendations are meant to increase sales or ad revenue, since this is the first priority of those who pay for them. As recommender systems match their recommendations with inferred preferences, we should not be surprised if the algorithm optimises for lucrative preferences and thus co-produces the preferences they mine. In this talk I will explain how the GDPR will help to break through this vicious circle, by constraining how people may be targeted.","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":"130273202","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}

{"title":"Darwin & Goliath: a white-label recommender-system as-a-service with automated algorithm-selection","authors":"J. Beel, Alan Griffin, Conor O'Shea","doi":"10.1145/3298689.3347059","DOIUrl":"https://doi.org/10.1145/3298689.3347059","url":null,"abstract":"Recommendations-as-a-Service (RaaS) ease the process for small and medium-sized enterprises (SMEs) to offer product recommendations to their customers. Current RaaS, however, suffer from a one-size-fits-all concept, i.e. they apply the same recommendation algorithm for all SMEs. We introduce Darwin & Goliath, a RaaS that features multiple recommendation frameworks (Apache Lucene, TensorFlow, ...), and identifies the ideal algorithm for each SME automatically. Darwin & Goliath further offers per-instance algorithm selection and a white label feature that allows SMEs to offer a RaaS under their own brand. Since November 2018, Darwin & Goliath has delivered more than 1m recommendations with a CTR = 0.5%.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"12 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":"130335097","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}

{"title":"Concept to code: deep learning for multitask recommendation","authors":"Omprakash Sonie","doi":"10.1145/3298689.3346957","DOIUrl":"https://doi.org/10.1145/3298689.3346957","url":null,"abstract":"Deep Learning has shown significant results in Computer Vision, Natural Language Processing, Speech and recommender systems. Promising techniques include Embedding, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and its variant Long Short-Term Memory (LSTM and Bi-directional LSTMs), Attention, Autoencoders, Generative Adversarial Networks (GAN) and Bidirectional Encoder Representations from Transformer (BERT). Multi-task learning (MTL) has led to successes in many applications of machine learning. We are proposing a tutorial for applying MTL for recommendation, improving recommendation and providing explanation. We cover few recent and diverse techniques which will be used for hands-on session. We believe that a self-contained tutorial giving good conceptual understanding of MTL technique with sufficient mathematical background along with actual code will be of immense help to RecSys participants.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"89 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":"130957806","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}

{"title":"Microsoft recommenders: tools to accelerate developing recommender systems","authors":"Scott Graham, Jun-Ki Min, Tao Wu","doi":"10.1145/3298689.3346967","DOIUrl":"https://doi.org/10.1145/3298689.3346967","url":null,"abstract":"The purpose of this demonstration is to highlight the content of the Microsoft Recommenders repository and show how it can be used to reduce the time involved in developing recommender systems. The open source repository provides python utilities to simplify common recommender-related data science work as well as example Jupyter notebooks that demonstrate use of the algorithms and tools under various environments.","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":"124262106","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}

{"title":"Interactive evaluation of recommender systems with SNIPER: an episode mining approach","authors":"Sandy Moens, Olivier Jeunen, Bart Goethals","doi":"10.1145/3298689.3346965","DOIUrl":"https://doi.org/10.1145/3298689.3346965","url":null,"abstract":"Recommender systems are typically evaluated using either offline methods, online methods, or through user studies. In this paper we take an episode mining approach to analysing recommender system data and we demonstrate how we can use SNIPER, a tool for interactive pattern mining, to analyse and understand the behaviour of recommender systems. We describe the required data format, and present a useful scenario of how a user can interact with the system to answer questions about the quality of recommendations.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"8 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":"123550313","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}