Proceedings of the 9th ACM Conference on Recommender Systems最新文献

{"title":"Predicting Online Performance of News Recommender Systems Through Richer Evaluation Metrics","authors":"Andrii Maksai, Florent Garcin, B. Faltings","doi":"10.1145/2792838.2800184","DOIUrl":"https://doi.org/10.1145/2792838.2800184","url":null,"abstract":"We investigate how metrics that can be measured offline can be used to predict the online performance of recommender systems, thus avoiding costly A-B testing. In addition to accuracy metrics, we combine diversity, coverage, and serendipity metrics to create a new performance model. Using the model, we quantify the trade-off between different metrics and propose to use it to tune the parameters of recommender algorithms without the need for online testing. Another application for the model is a self-adjusting algorithm blend that optimizes a recommender's parameters over time. We evaluate our findings on data and experiments from news websites.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114918920","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 Recommender Systems based on Data Stream Management Systems","authors":"C. Ludmann","doi":"10.1145/2792838.2796544","DOIUrl":"https://doi.org/10.1145/2792838.2796544","url":null,"abstract":"In this paper, I present a novel approach for implementing a stream-based Recommender System (RecSys). I propose to add RecSys operators to an application-independent Data Stream Management System (DSMS) to allow writing continuous queries over data streams that calculate personalized sets of recommendations. That empowers RecSys providers to create a custom RecSys by writing queries in a declarative query language. This approach ensures a flexible and extendable usage of RecSys functions in different settings and benefits from matured features of DSMSs.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121698902","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":"Uncovering Systematic Bias in Ratings across Categories: a Bayesian Approach","authors":"Fangjian Guo, D. Dunson","doi":"10.1145/2792838.2799683","DOIUrl":"https://doi.org/10.1145/2792838.2799683","url":null,"abstract":"Recommender systems are routinely equipped with standardized taxonomy that associates each item with one or more categories or genres. Although such information does not directly imply the quality of an item, the distribution of ratings vary greatly across categories, e.g. animation movies may generally receive higher ratings than action movies. While it is a natural outcome given the diversity and heterogeneity of both users and items, it makes directly aggregated ratings, which are commonly used to guide users' choice by reflecting the overall quality of an item, incomparable across categories and hence prone to fairness and diversity issues. This paper aims to uncover and calibrate systematic category-wise biases for discrete-valued ratings. We propose a novel Bayesian multiplicative probit model that treats the inflation or deflation of mean rating for a combination of categories as multiplicatively contributed from category-specific parameters. The posterior distribution of those parameters, as inferred from data, can capture the bias for all possible combinations of categories, thus enabling statistically efficient estimation and principled rating calibration.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131052591","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 2015 and the YOOCHOOSE Dataset","authors":"David Ben-Shimon, Alexander Tsikinovsky, Michael Friedmann, Bracha Shapira, L. Rokach, J. Hörle","doi":"10.1145/2792838.2798723","DOIUrl":"https://doi.org/10.1145/2792838.2798723","url":null,"abstract":"The 2015 ACM Recommender Systems Challenge offered the opportunity to work on a large-scale e-commerce dataset from a big retailer in Europe which is accepting recommender system as a service from YOOCHOOSE. Participants tackled the problem of predicting what items a user intends to purchase, if any, given a click sequence performed during an activity session on the e-commerce website. The challenge ran for seven months and was very successful, attracting 850 teams from 49 countries which submitted a total of 5,437 solutions. The winners of the challenge scored approximately 50% of the maximum score, which we considered as an impressive achievement. In this paper we provide a brief overview of the challenge and its results.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131578237","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 Recommendation Game: Using a Game-with-a-Purpose to Generate Recommendation Data","authors":"Sam Banks, Rachael Rafter, Barry Smyth","doi":"10.1145/2792838.2799675","DOIUrl":"https://doi.org/10.1145/2792838.2799675","url":null,"abstract":"This paper describes a casual Facebook game to capture recommendation data as a side-effect of gameplay. We show how this data can be used to make successful recommendations as part of a live-user trial.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131164490","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":"3rd International Workshop on News Recommendation and Analytics (INRA 2015)","authors":"J. Gulla, Bei Yu, Özlem Özgöbek, Nafiseh Shabib","doi":"10.1145/2792838.2798721","DOIUrl":"https://doi.org/10.1145/2792838.2798721","url":null,"abstract":"The 3rd International Workshop on News Recommendation and Analytics (INRA 2015) is held in conjunction with RecSys 2015 Conference in Vienna, Austria. This paper presents a brief summary of the INRA 2015. This workshop aims to create an interdisciplinary community that addresses design issues in news recommender systems and news analytics, and promote fruitful collaboration opportunities between researchers, media companies and practitioners. We have a keynote speaker and an invited demo presentation in addition to 4 papers accepted in this workshop.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132628626","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":"Scaling Up Recommendation Services in Many Dimensions","authors":"B. Németh","doi":"10.1145/2792838.2799499","DOIUrl":"https://doi.org/10.1145/2792838.2799499","url":null,"abstract":"Gravity R&D has been providing recommendation engines as SaaS solutions since 2009. The company has a strong research focus and recommendation quality has always been their primary differentiating factor. Widely used or open source recommendation algorithms are of little use to our technology team as a result of the superiority of our in-house developed, proprietary algorithms. Gravity R&D experienced many challenges while scaling up their services. The sheer quantity of data handled on a daily basis increased exponentially. This presentation will cover how overcoming these challenges permanently shaped our algorithms and system architecture used to generate these recommendations. Serving personalized recommendations requires real-time computation and data access for every single request. To generate responses in real-time, current user inputs have to be compared against their history in order to deliver accurate recommendations. We then combine this user information with specific details about available items as the next step in the recommendation process. It becomes more difficult to provide accurate recommendations as the number of transactions and items increase. It also becomes difficult because this type of analysis requires the combination of multiple heterogeneous algorithms that all require different inputs. Initially, the architecture was designed for MF based models and serving huge numbers of requests but with a limited number of items. Now, Gravity is using MF, neighborhood based models and metadata based models to generate recommendations for millions of items within their databases. This required a shift from a monolithic architecture with in-process caching to a more service oriented architecture with multi-layer caching. As a result of an increase in the number of components and number of clients, managing the infrastructure can be quite difficult. Even with these challenges, we don't believe that it is worthwhile to use a fully distributed system. It adds unneeded complexity, resources, and overhead to the system. We prefer an approach of firstly optimizing current algorithms and architecture and only moving to a distributed system when no other options are left.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122302797","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 the User Experience during Cold Start through Choice-Based Preference Elicitation","authors":"Mark P. Graus, M. Willemsen","doi":"10.1145/2792838.2799681","DOIUrl":"https://doi.org/10.1145/2792838.2799681","url":null,"abstract":"We studied an alternative choice-based interface for preference elicitation during the cold start phase and compared it directly with a standard rating-based interface. In this alternative interface users started from a diverse set covering all movies and iteratively narrowed down through a matrix factorization latent feature space to smaller sets of items based on their choices. The results show that compared to a rating-based interface, the choice-based interface requires less effort and results in more satisfying recommendations, showing that it might be a promising candidate for alleviating the cold start problem of new users.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126955530","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":"CNARe","authors":"Guilherme A. de Sousa, M. A. Diniz, M. Brandão, Mirella M. Moro","doi":"10.1145/2792838.2796553","DOIUrl":"https://doi.org/10.1145/2792838.2796553","url":null,"abstract":"We present CNARe, an easy-to-use online system that shows personalized collaboration recommendations to researchers. It also provides visualizations and metrics that allow to investigate how the recommendations affect a co-authorship social network and other analyses.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128637752","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":"Context-Aware Event Recommendation in Event-based Social Networks","authors":"A. Q. Macedo, L. Marinho, Rodrygo L. T. Santos","doi":"10.1145/2792838.2800187","DOIUrl":"https://doi.org/10.1145/2792838.2800187","url":null,"abstract":"The Web has grown into one of the most important channels to communicate social events nowadays. However, the sheer volume of events available in event-based social networks (EBSNs) often undermines the users' ability to choose the events that best fit their interests. Recommender systems appear as a natural solution for this problem, but differently from classic recommendation scenarios (e.g. movies, books), the event recommendation problem is intrinsically cold-start. Indeed, events published in EBSNs are typically short-lived and, by definition, are always in the future, having little or no trace of historical attendance. To overcome this limitation, we propose to exploit several contextual signals available from EBSNs. In particular, besides content-based signals based on the events' description and collaborative signals derived from users' RSVPs, we exploit social signals based on group memberships, location signals based on the users' geographical preferences, and temporal signals derived from the users' time preferences. Moreover, we combine the proposed signals for learning to rank events for personalized recommendation. Thorough experiments using a large crawl of Meetup.com demonstrate the effectiveness of our proposed contextual learning approach in contrast to state-of-the-art event recommenders from the literature.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125873008","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}