Proceedings of the Eleventh ACM Conference on Recommender Systems最新文献

{"title":"Rethinking Collaborative Filtering: A Practical Perspective on State-of-the-art Research Based on Real World Insights","authors":"Noam Koenigstein","doi":"10.1145/3109859.3109919","DOIUrl":"https://doi.org/10.1145/3109859.3109919","url":null,"abstract":"A decade has passed since the seminal Netflix Prize competition and Collaborative Filtering (CF) models are still at the forefront of Recommender System research. Significant progress has been achieved over this time, yet key aspects of the basic problem formulation have not been seriously challenged. Most state-of-the-art models still assume a supervised model in which the ultimate goal is to predict future user-item interactions based on the generalization of historical data. We wish to initiate a discussion on some key assumptions behind much of the mainstream research: What is the difference between predicting future user actions and optimizing Key Performance Indicators (KPIs)? Does a trade-off between accuracy and diversity really exist? Is supervised CF based on historical data still relevant in the age of modern reinforcement learning models such as contextual bandits? What evaluation metrics can be used prior to online experimentations and what are their limitations? Our main thesis is that at the core of all these issues lies a gap between most Collaborative Filtering models and the true objective of industry systems.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114149022","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":"Improving Higher Education: Learning Analytics & Recommender Systems Research","authors":"G. Karypis","doi":"10.1145/3109859.3109870","DOIUrl":"https://doi.org/10.1145/3109859.3109870","url":null,"abstract":"An enduring issue in higher education is student retention to successful graduation. Studies in the U.S. report that average six-year graduation rates across higher-education institutions is 59% and have remained relatively stable over the last 15 years. For those that do complete a college degree, less than half complete within four-years. Requiring additional terms or leaving college without receiving a bachelor's degree has high human and monetary costs and deprives students from the economic benefits of a college credential (over $1 million in a lifetime and even higher in STEM fields). Moreover, when students do not succeed in graduating, local and national communities struggle to create an educated workforce. Estimates indicate that by 2020 over 64% of the jobs in the U.S. will require at least some post-secondary education. These challenges have been recognized by the U.S. National Research Council, which identified that there is a critical need to develop innovative approaches to enable higher-education institutions retain students, ensure their timely graduation, and are well-trained and workforce ready in their field of study. Failure to do so represents a significant problem as it deprives the U.S. of the highly skilled workforce that it needs to successfully compete in the modern world. This talk describes various efforts under way to develop \"Big Data\" methods to analyze in a comprehensive manner, the large and diverse types of education and learning-related data in order to improve undergraduate education. These methods are motivated by and are designed to address various interrelated issues that have a significant impact on college student success and include: (i) academic pathways towards successful and timely graduation from the student perspective; (ii) effective pedagogy by instructors; and (iii) retention and persistence of students from the institutional and advisor perspective. In addition, the talk will discuss areas in which research methods and approaches originally developed by the recommender systems community can be applied to this domain.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116089325","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":"DLRS 2017: Second Workshop on Deep Learning for Recommender Systems","authors":"Balázs Hidasi, Alexandros Karatzoglou, Oren Sar Shalom, S. Dieleman, Bracha Shapira, D. Tikk","doi":"10.1145/3109859.3109953","DOIUrl":"https://doi.org/10.1145/3109859.3109953","url":null,"abstract":"Deep learning methods became widely popular in the recommender systems community in 2016, in part thanks to the previous event of the DLRS workshop series. Now, deep learning has been embedded in the main conference as well and initial research directions have started forming, so the role of DLRS 2017 is to encourage starting new research directions, incentivize the application of very recent techniques from deep learning, and provide a venue for specialized discussion of this topic.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124593006","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":"The 1st Workshop on Intelligent Recommender Systems by Knowledge Transfer & Learning: (RecSysKTL)","authors":"Yong Zheng, Weike Pan, Shaghayegh Sherry Sahebi, Ignacio Fernández","doi":"10.1145/3109859.3109951","DOIUrl":"https://doi.org/10.1145/3109859.3109951","url":null,"abstract":"Cross-domain recommender systems and transfer learning approaches are useful to help integrate knowledge from different places, so that we alleviate some existing problems (such as the cold-start problem), or improve the quality of recommender systems. With the advantages of these techniques, we host the first international workshop on intelligent recommender systems by knowledge transfer and learning (RecSysKTL) to provide such a forum for academia researchers and application developers from around the world to present their work and discuss exciting research ideas or outcomes. The workshop is held in conjunction with the ACM Conference on Recommender Systems 2017 on August 27th at Como, Italy.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130807716","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":"VAMS 2017: Workshop on Value-Aware and Multistakeholder Recommendation","authors":"R. Burke, G. Adomavicius, Ido Guy, Jan Krasnodebski, L. Pizzato, Yi Zhang, Himan Abdollahpouri","doi":"10.1145/3109859.3109957","DOIUrl":"https://doi.org/10.1145/3109859.3109957","url":null,"abstract":"In this paper, we summarize VAMS 2017 - a workshop on value-aware and multistakeholder recommendation co-located with RecSys 2017. The workshop encouraged forward-thinking papers in this new area of recommender systems research and obtained a diverse set of responses ranging from application results to research overviews.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123593934","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 Semantic-Aware Profile Updating Model for Text Recommendation","authors":"Hossein Rahmatizadeh Zagheli, Hamed Zamani, A. Shakery","doi":"10.1145/3109859.3109904","DOIUrl":"https://doi.org/10.1145/3109859.3109904","url":null,"abstract":"Content-based recommender systems (CBRSs) rely on user-item similarities that are calculated between user profiles and item representations. Appropriate representation of each user profile based on the user's past preferences can have a great impact on user's satisfaction in CBRSs. In this paper, we focus on text recommendation and propose a novel profile updating model based on previously recommended items as well as semantic similarity of terms calculated using distributed representation of words. We evaluate our model using two standard text recommendation datasets: TREC-9 Filtering Track and CLEF 2008-09 INFILE Track collections. Our experiments investigate the importance of both past recommended items and semantic similarities in recommendation performance. The proposed profile updating method significantly outperforms the baselines, which confirms the importance of incorporating semantic similarities in the profile updating task.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124519576","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":"An Elementary View on Factorization Machines","authors":"S. Prillo","doi":"10.1145/3109859.3109892","DOIUrl":"https://doi.org/10.1145/3109859.3109892","url":null,"abstract":"Factorization Machines (FMs) are a model class capable of learning pairwise (and in general higher order) feature interactions from high dimensional, sparse data. In this paper we adopt an elementary view on FMs. Specifically, we view FMs as a sum of simple surfaces - a hyperplane plus several squared hyperplanes - in the original feature space. This elementary view, although equivalent to that of low rank matrix factorization, is geometrically more intuitive and points to some interesting generalizations. Led by our intuition, we challenge our understanding of the inductive bias of FMs by showing a simple dataset where FMs counterintuitively fail to learn the weight of the interaction between two features. We discuss the reasons, and mathematically formulate and prove a form of this limitation. Also inspired by our elementary view, we propose modeling intermediate orders of interaction, such as 1.5-way FMs. Beyond the specific proposals, the goal of this paper is to expose our thoughts and ideas to the research community in an effort to take FMs to the next level.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125713054","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":"Second Workshop on Health Recommender Systems: (HealthRecSys 2017)","authors":"David Elsweiler, Santiago Hors-Fraile, Bernd Ludwig, A. Said, Hanna Schäfer, C. Trattner, Helma Torkamaan, André Calero Valdez","doi":"10.1145/3109859.3109955","DOIUrl":"https://doi.org/10.1145/3109859.3109955","url":null,"abstract":"The 2017 Workshop on Health Recommender Systems was held in conjunction with the 2017 ACM Conference on Recommender Systems in Como, Italy. Following the fists workshop in 2016, the focus of this workshop was on enhancing the results of the first workshop by elaborating discussions on the topics, attracting scientist from other domains, finding cross-domain collaboration, and establishing shared infrastructures.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125875470","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":"Online Learning to Rank for Recommender Systems","authors":"Daan Odijk, Anne Schuth","doi":"10.1145/3109859.3109925","DOIUrl":"https://doi.org/10.1145/3109859.3109925","url":null,"abstract":"Blendle is a New York Times backed startup that builds a platform where users can explore and support the world's best journalism. Users can read all content from 120 publications and only pay for what they read. Every morning, at Blendle, we have a huge cold-start problem when over 8.000 new articles from the latest editions of newspapers arrive in our system. At that moment, these articles are read by virtually no-one and we are tasked with sending out personalised newsletters to over 1 million users. We can thus not rely on collaborative filtering type of recommendations, nor can we use the popularity of the articles as clues for what our user might want to read. We overcome our cold-start problem by a mix of curation by our editorial team and an automated analysis of the content of these articles. We extract named entities, semantic links, authors, the language and plenty of stylometrics. For each of our users, we build a very fine grained profile based on the attributes of the articles that they read. The combination of enriched articles and user profiles is fed into our machine learning pipeline. We are currently experimenting with an online learning to rank setup, where each of our users is exposed to a slightly perturbed version of our ranking model. We observe the interactions of our users to infer in which direction we should be updating the model. Our editorial team gets up at around 5am every morning to read what was published over night. They are done reading and recommending their selection of articles around 8am, which is also the time we would ideally send out the newsletter so that our users, on their commute to work, can read our newsletter. These timing restrictions pose yet another challenge: our content analysis and machine learning pipeline needs to be really fast. We solve this by using a streaming infrastructure build on Kafka. In this infrastructure, an article is analysed and scored for relevance towards each of our users as soons as it arrives. This has the advantage that at 8am, when our editorial team is done reading, personalisation is much more lightweight. We use the precomputed relevance scores and balance them with diversity to arrive at a unique ranking for each of our users. In this talk, I will detail how we enrich articles in a streaming fashion and how we use online learning methods to learn a ranking model. I will also talk about how we deal with the time constraints of the problem we are trying to solve.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121691725","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":"Translation-based Recommendation","authors":"Ruining He, Wang-Cheng Kang, Julian McAuley","doi":"10.1145/3109859.3109882","DOIUrl":"https://doi.org/10.1145/3109859.3109882","url":null,"abstract":"Modeling the complex interactions between users and items as well as amongst items themselves is at the core of designing successful recommender systems. One classical setting is predicting users' personalized sequential behavior (or 'next-item' recommendation), where the challenges mainly lie in modeling 'third-order' interactions between a user, her previously visited item(s), and the next item to consume. Existing methods typically decompose these higher-order interactions into a combination of pairwise relationships, by way of which user preferences (user-item interactions) and sequential patterns (item-item interactions) are captured by separate components. In this paper, we propose a unified method, TransRec, to model such third-order relationships for large-scale sequential prediction. Methodologically, we embed items into a 'transition space' where users are modeled as translation vectors operating on item sequences. Empirically, this approach outperforms the state-of-the-art on a wide spectrum of real-world datasets. Data and code are available at https://sites.google.com/a/eng.ucsd.edu/ruining-he/.","PeriodicalId":417173,"journal":{"name":"Proceedings of the Eleventh ACM Conference on Recommender Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121488069","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}