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

{"title":"Fourth international workshop on health recommender systems (HealthRecSys 2019)","authors":"David Elsweiler, Bernd Ludwig, A. Said, Hanna Schäfer, Helma Torkamaan, C. Trattner","doi":"10.1145/3298689.3347053","DOIUrl":"https://doi.org/10.1145/3298689.3347053","url":null,"abstract":"HealthRecSys 2019 was the 4th International Workshop on Health Recommender Systems held in conjunction with the 2019 ACM Conference on Recommender Systems in Copenhagen, Denmark. This workshop followed on from of the previous workshop in 2018 [4] and focused on the application and potentials of recommender systems on health promotion, health care and health-related topics. By engaging the discussion and representation of health domains into recommender systems, this workshop facilitated the cross-domain collaborations and exchange of knowledge and infrastructure.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"7 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":"126232483","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":"Time slice imputation for personalized goal-based recommendation in higher education","authors":"Weijie Jiang, Z. Pardos","doi":"10.1145/3298689.3347030","DOIUrl":"https://doi.org/10.1145/3298689.3347030","url":null,"abstract":"Learners are often faced with the following scenario: given a goal for the future, and what they have learned in the past, what should they do now to best achieve their goal? We build on work utilizing deep learning to make inferences about how past actions correspond to future outcomes and enhance this work with a novel application of backpropagation to learn per-user optimized next actions. We apply this technique to two datasets, one from a university setting in which courses can be recommended towards preparation for a target course, and one from a massive open online course (MOOC) in which course pages can be recommended towards quiz preparation. In both cases, our algorithm is applied to recommend actions the learner can take to maximize a desired future achievement objective, given their past actions and performance.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"43 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":"125093642","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":"RecSys challenge 2019: session-based hotel recommendations","authors":"Peter Knees, Yashar Deldjoo, Farshad Bakhshandegan Moghaddam, J. Adamczak, G. Leyson, Philipp Monreal","doi":"10.1145/3298689.3346974","DOIUrl":"https://doi.org/10.1145/3298689.3346974","url":null,"abstract":"The workshop features presentations of accepted contributions to the RecSys Challenge 2019 organized by trivago, TU Wien, Politecnico di Bari, and Karlsruhe Institute of Technology. In the challenge, which originates from the domain of online travel recommender systems, participants had to build a click-prediction model based on user session interactions. Predictions were submitted in the form of a list of suggested accommodations and evaluated on an offline data set that contained the information what accommodation was clicked in the later part of a session. The data set contains anonymized information about almost 16 million session interactions of over 700.000 users visiting the trivago website. The challenge was well received with 1509 teams that signed up and 607 teams teams that submitted a valid solution. 3452 solutions were submitted during the course of the challenge.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"162 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":"127201056","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":"A comparison of calibrated and intent-aware recommendations","authors":"Mesut Kaya, D. Bridge","doi":"10.1145/3298689.3347045","DOIUrl":"https://doi.org/10.1145/3298689.3347045","url":null,"abstract":"Calibrated and intent-aware recommendation are recent approaches to recommendation that have apparent similarities. Both try, to a certain extent, to cover the user's interests, as revealed by her user profile. In this paper, we compare them in detail. On two datasets, we show the extent to which intent-aware recommendations are calibrated and the extent to which calibrated recommendations are diverse. We consider two ways of defining a user's interests, one based on item features, the other based on subprofiles of the user's profile. We find that defining interests in terms of subprofiles results in highest precision and the best relevance/diversity trade-off. Along the way, we define a new version of calibrated recommendation and three new evaluation metrics.","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":"127745715","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":"Variational low rank multinomials for collaborative filtering with side-information","authors":"Ehtsham Elahi, Wei Wang, Dave Ray, Aish Fenton, T. Jebara","doi":"10.1145/3298689.3347036","DOIUrl":"https://doi.org/10.1145/3298689.3347036","url":null,"abstract":"We are interested in Bayesian models for collaborative filtering that incorporate side-information or metadata about items in addition to user-item interaction data. We present a simple and flexible framework to build models for this task that exploit the low-rank structure in user-item interaction datasets. Although the resulting models are non-conjugate, we develop an efficient technique for approximating posteriors over model parameters using variational inference. We borrow the \"re-parameterization trick\" from Bayesian deep learning literature to enable variational inference in our models. The resulting approximate Bayesian inference algorithm is scalable and can handle large scale datasets. We demonstrate our ideas on three real world datasets where we show competitive performance against widely used baselines.","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":"127385023","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":"Workshop on context-aware recommender systems","authors":"G. Adomavicius, Konstantin Bauman, B. Mobasher, F. Ricci, A. Tuzhilin, Moshe Unger","doi":"10.1145/3298689.3346954","DOIUrl":"https://doi.org/10.1145/3298689.3346954","url":null,"abstract":"Contextual information has been widely recognized as an important modeling dimension both in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused context-aware recommender systems (CARS), many interesting problems remain under-explored. The CARS 2019 workshop provides a venue for presenting and discussing approaches for next generation of CARS and application domains that may require a variety of dimensions of contexts and cope with its dynamic properties.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"2 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":"127314650","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":"Driving content recommendations by building a knowledge base using weak supervision and transfer learning","authors":"S. Deb","doi":"10.1145/3298689.3346963","DOIUrl":"https://doi.org/10.1145/3298689.3346963","url":null,"abstract":"With 2.2 million subscribers and two hundred million content views, Chegg is a centralized hub where students come to get help with writing, science, math, and other educational needs. In order to impact a student's learning capabilities we present personalized content to students. Student needs are unique based on their learning style, studying environment and many other factors. Most students will engage with a subset of the products and contents available at Chegg. In order to recommend personalized content to students we have developed a generalized Machine Learning Pipeline that is able to handle training data generation and model building for a wide range of problems. We generate a knowledge base with a hierarchy of concepts and associate student-generated content, such as chat-room data, equations, chemical formulae, reviews, etc with concepts in the knowledge base. Collecting training data to generate different parts of the knowledge base is a key bottleneck in developing NLP models. Employing subject matter experts to provide annotations is prohibitively expensive. Instead, we use weak supervision and active learning techniques, with tools such as snorkel[2], an open source project from Stanford, to make training data generation dramatically easier. With these methods, training data is generated by using broad stroke filters and high precision rules. The rules are modeled probabilistically to incorporate dependencies. Features are generated using transfer learning[1] from language models for classification tasks. We explored several language models and the best performance was from sentence embeddings with skip-thought vectors predicting the previous and the next sentence. The generated structured information is then used to improve product features, and enhance recommendations made to students. In this presentation I will talk about efficient methods of tagging content with categories that come from a knowledge base. Using this information we provide relevant content recommendations to students coming to Chegg for online tutoring, studying flashcards and practicing problems.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"226 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":"116846739","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":"Latent factor models and aggregation operators for collaborative filtering in reciprocal recommender systems","authors":"James Neve, I. Palomares","doi":"10.1145/3298689.3347026","DOIUrl":"https://doi.org/10.1145/3298689.3347026","url":null,"abstract":"Online dating platforms help to connect people who might potentially be a good match for each other. They have exerted a significant societal impact over the last decade, such that about one third of new relationships in the US are now started online, for instance. Recommender Systems are widely utilized in online platforms that connect people to people in e.g. online dating and recruitment sites. These recommender approaches are fundamentally different from traditional user-item approaches (such as those operating on movie and shopping sites), in that they must consider the interests of both parties jointly. Latent factor models have been notably successful in the area of user-item recommendation, however they have not been investigated within user-to-user domains as of yet. In this study, we present a novel method for reciprocal recommendation using latent factor models. We also provide a first analysis of the use of different preference aggregation strategies, thereby demonstrating that the aggregation function used to combine user preference scores has a significant impact on the outcome of the recommender system. Our evaluation results report significant improvements over previous nearest-neighbour and content-based methods for reciprocal recommendation, and show that the latent factor model can be used effectively on much larger datasets than previous state-of-the-art reciprocal recommender systems.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"6 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":"133027743","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":"Homepage personalization at spotify","authors":"Oguz Semerci, Alois Gruson, Catherinee Edwards, B. Lacker, Clay Gibson, Vladan Radosavljevic","doi":"10.1145/3298689.3346977","DOIUrl":"https://doi.org/10.1145/3298689.3346977","url":null,"abstract":"We aim to surface the best of Spotify for each user on the Home page by providing a personalized space where users can find recommendations of playlists, albums, artists, podcasts tailored to their individual preferences. Hundreds of millions of users listen to music on Spotify each month, with more than 50 million daily active users on the Homepage alone. The quality of the recommendations on Home depends on a multi-armed bandit framework that balances exploration and exploitation and allows us to adapt quickly to changes in user preferences. We employ counterfactual training and reasoning to evaluate new algorithms without having to always rely on A/B testing or randomized data collection experiments [3]. In this talk, we explain the methods and technologies used in the end-to-end process of homepage personalization and demonstrate a case study where we show improved user satisfaction over a popularity-based baseline. In addition, we present some of the challenges we faced in implementing such machine learning solutions in a production environment at scale and the approaches used to address them. The first challenge stems from the fact that training and offline evaluation of machine learning methods from incomplete logged feedback data requires robust off-policy estimators that account for several forms of bias [1, 2]. The ability to quickly sanity check and gain confidence in the methods we use in the production system is a crucial foundation for developing and maintaining effective algorithms. We demonstrate how we used a single-feature model, optimized for impression-to-click rate, to validate, and improve if necessary, the methods we use for off-policy estimation and accounting for position bias. Lastly, the business metrics we optimize for do not always reflect the expectations of all users of the Home page at a granular level. Consider a niche, daily podcast producing independent, fact-based news every morning. A small segment of Spotify customers might want to see that content on top of their Home page every morning. We present simple but informative metrics we developed to validate our model's ability to account for such habitual behaviors of our customers.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"358 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":"115877069","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":"FiBiNET","authors":"Tongwen Huang, Zhiqi Zhang, Junlin Zhang","doi":"10.1145/3298689.3347043","DOIUrl":"https://doi.org/10.1145/3298689.3347043","url":null,"abstract":"Advertising and feed ranking are essential to many Internet companies such as Facebook and Sina Weibo. Among many real-world advertising and feed ranking systems, click through rate (CTR) prediction plays a central role. There are many proposed models in this field such as logistic regression, tree based models, factorization machine based models and deep learning based CTR models. However, many current works calculate the feature interactions in a simple way such as Hadamard product and inner product and they care less about the importance of features. In this paper, a new model named FiBiNET as an abbreviation for Feature Importance and Bilinear feature Interaction NETwork is proposed to dynamically learn the feature importance and fine-grained feature interactions. On the one hand, the FiBiNET can dynamically learn the importance of features via the Squeeze-Excitation network (SENET) mechanism; on the other hand, it is able to effectively learn the feature interactions via bilinear function. We conduct extensive experiments on two real-world datasets and show that our shallow model outperforms other shallow models such as factorization machine(FM) and field-aware factorization machine(FFM). In order to improve performance further, we combine a classical deep neural network(DNN) component with the shallow model to be a deep model. The deep FiBiNET consistently outperforms the other state-of-the-art deep models such as DeepFM and extreme deep factorization machine(XdeepFM).","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"27 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":"125372482","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}