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

{"title":"Elsevier Journal Finder: Recommending Journals for your Paper","authors":"Ning Kang, Marius A. Doornenbal, B. Schijvenaars","doi":"10.1145/2792838.2799663","DOIUrl":"https://doi.org/10.1145/2792838.2799663","url":null,"abstract":"Rejection is the norm in academic publishing. One of the main reasons for rejections is that the topics of the submitted papers are not relevant to the scope of the journal, even when the papers themselves are excellent. Submission to a journal that fits well with the publication may avoid this issue. A system that is able to suggest journals that have published similar articles to the submitted papers may help authors choose where to submit. The Elsevier journal finder, a freely available online service, is one of the most comprehensive journal recommender systems, covering all scientific domains and more than 2,900 per-reviewed Elsevier journals. The system uses natural language processing for feature generation, and Okapi BM25 matching for the recommendation algorithm. The procedure is to paste text, such as an abstract, and get a list of recommend journals and relevant metadata. The website URL is http://journalfinder.elsevier.com.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"20 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120910310","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":"POI Recommendation: Towards Fused Matrix Factorization with Geographical and Temporal Influences","authors":"Jean-Benoît Griesner, T. Abdessalem, Hubert Naacke","doi":"10.1145/2792838.2799679","DOIUrl":"https://doi.org/10.1145/2792838.2799679","url":null,"abstract":"Providing personalized point-of-interest (POI) recommendation has become a major issue with the rapid emergence of location-based social networks (LBSNs). Unlike traditional recommendation approaches, the LBSNs application domain comes with significant geographical and temporal dimensions. Moreover most of traditional recommendation algorithms fail to cope with the specific challenges implied by these two dimensions. Fusing geographical and temporal influences for better recommendation accuracy in LBSNs remains unexplored, as far as we know. We depict how matrix factorization can serve POI recommendation, and propose a novel attempt to integrate both geographical and temporal influences into matrix factorization. Specifically we present GeoMF-TD, an extension of geographical matrix factorization with temporal dependencies. Our experiments on a real dataset shows up to 20% benefit on recommendation precision.","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":"133573609","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":"Letting Users Choose Recommender Algorithms: An Experimental Study","authors":"Michael D. Ekstrand, Daniel Kluver, F. M. Harper, J. Konstan","doi":"10.1145/2792838.2800195","DOIUrl":"https://doi.org/10.1145/2792838.2800195","url":null,"abstract":"Recommender systems are not one-size-fits-all; different algorithms and data sources have different strengths, making them a better or worse fit for different users and use cases. As one way of taking advantage of the relative merits of different algorithms, we gave users the ability to change the algorithm providing their movie recommendations and studied how they make use of this power. We conducted our study with the launch of a new version of the MovieLens movie recommender that supports multiple recommender algorithms and allows users to choose the algorithm they want to provide their recommendations. We examine log data from user interactions with this new feature to under-stand whether and how users switch among recommender algorithms, and select a final algorithm to use. We also look at the properties of the algorithms as they were experienced by users and examine their relationships to user behavior. We found that a substantial portion of our user base (25%) used the recommender-switching feature. The majority of users who used the control only switched algorithms a few times, trying a few out and settling down on an algorithm that they would leave alone. The largest number of users prefer a matrix factorization algorithm, followed closely by item-item collaborative filtering; users selected both of these algorithms much more often than they chose a non-personalized mean recommender. The algorithms did produce measurably different recommender lists for the users in the study, but these differences were not directly predictive of user choice.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"29 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":"114416247","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":"Recommendations in Travel","authors":"O. Zoeter","doi":"10.1145/2792838.2799500","DOIUrl":"https://doi.org/10.1145/2792838.2799500","url":null,"abstract":"Recommender systems have received much attention in recent years, and they have been successfully applied in many different domains. With each domain come new constraints that require system designers to make choices about how to apply and extend generic algorithms in their context. Booking.com is planet earth's number one accommodation reservation site. The accommodation recommendation problem that it needs to solve has several interesting and unique challenges that make that a straightforward matrix factorization or a basic bi-linear model are not sufficient to provide the required predictions. In this talk, we will discuss several of the challenges we have encountered and solutions we have developed.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"240 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":"116129549","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 (Persuasive?) Speech on Automated Persuasion","authors":"O. Stock","doi":"10.1145/2792838.2799503","DOIUrl":"https://doi.org/10.1145/2792838.2799503","url":null,"abstract":"Philosophers of language have taught us that at the basis of language production there is the intention to change the state of the world by intervening linguistically on other agents. Persuasion, being the process of influencing attitudes, beliefs, behaviors, mood of a target, is a matter of stronger emphasis. Argumentation is just one resource to persuasion; it has been studied since the times of Aristotle and now for quite some time in artificial intelligence. The peripheral route to persuasion [1] is a different modality, one that is based on indirect, evocative, aesthetic aspects of the message. Automated intelligent persuasion of this sort (and also defense from inappropriate persuasion) is a research area close to producing usable results, both through creative production of language expressions, and through other forms of communication. The traditional goal of human-oriented information technology is mostly to offer services. With intelligent persuasive interfaces, instead, the overall goal is to produce an effect on humans, to influence their beliefs, their attitudes and eventually their actions and overall behavior. The area of intelligent persuasion has the potential to change the picture radically in the world of advertising and of social influencing. Computer-based systems can be flexible, and starting from goals they have to pursue, they can take into account the situation and the specific target, adapt the messages in appropriate ways and assess the outcome. In addition, the availability of very large amounts of data which can be exploited also in real time provides unprecedented possibilities. It is easy now to predict the following developments for the advertising sector: reduction in time to market and extension of possible occasions for advertisement; overall reduction of off target messages, eliminating the less relevant for the individual in a given situation; more attention to the wearing out of the message and to the need for planning variants and connected messages across time and space; contextual personalization, on the basis of audience profile and dynamic model (emotional state, beliefs, goals, etc.) and situational information; interactivity; audience reaction monitoring and system feedback on message effectiveness. In the Per Te project we have explored several areas concerned with intelligent persuasion. One topic is concerned with getting the attention and evoking a desired concept by means of original linguistic expressions. A main theme is the automatic production of flexible creative messages [2]. The approach is based on developing specific techniques, mostly corpus based, for producing variations of given expressions [3]. Another theme is concerned with ambient intelligence and peripheral displays. A novel technology is able to indirectly but purposefully impact on the behavior of a co-located group of people. Continuous recognition of each individual's focus of attention and activity is used to drive a tableto","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"39 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":"121197810","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 Role of User Location in Personalized Search and Recommendation","authors":"Ido Guy","doi":"10.1145/2792838.2799502","DOIUrl":"https://doi.org/10.1145/2792838.2799502","url":null,"abstract":"With mobile devices, users no longer access the web from specific locations, but virtually from anywhere. How does this affect our ability to provide personalized information for users' In this talk, I will discuss the influence of location activity on users' information needs and how a better understanding of these needs can help enhance web applications in which personalization plays a central role.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"71 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":"126758448","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":"Beyond \"Hitting the Hits\": Generating Coherent Music Playlist Continuations with the Right Tracks","authors":"D. Jannach, Lukas Lerche, Iman Kamehkhosh","doi":"10.1145/2792838.2800182","DOIUrl":"https://doi.org/10.1145/2792838.2800182","url":null,"abstract":"Automated playlist generation is a special form of music recommendation and a common feature of digital music playing applications. A particular challenge of the task is that the recommended items should not only match the general listener's preference but should also be coherent with the most recently played tracks. In this work, we propose a novel algorithmic approach and optimization scheme to generate playlist continuations that address these requirements. In our approach, we first use collections of shared music playlists, music metadata, and user preferences to select suitable tracks with high accuracy. Next, we apply a generic re-ranking optimization scheme to generate playlist continuations that match the characteristics of the last played tracks. An empirical evaluation on three collections of shared playlists shows that the combination of different input signals helps to achieve high accuracy during track selection and that the re-ranking technique can both help to balance different quality optimization goals and to further increase accuracy.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"44 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":"126782487","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":"Crowd Sourcing, with a Few Answers: Recommending Commuters for Traffic Updates","authors":"E. Daly, M. Berlingerio, François Schnitzler","doi":"10.1145/2792838.2799673","DOIUrl":"https://doi.org/10.1145/2792838.2799673","url":null,"abstract":"Real-time traffic awareness applications are playing an ever increasing role understanding and tackling traffic congestion in cities. First-hand accounts from drivers witnessing an incident is an invaluable source of information for traffic managers. Nowadays, drivers increasingly contact control rooms through social media to report on journey times, accidents or road weather conditions. These new interactions allow traffic controllers to engage users, and in particular to query them for information rather than passively collecting it. Querying participants presents the challenge of which users to probe for updates about a specific situation. In order to maximise the probability of a user responding and the accuracy of the information, we propose a strategy which takes into account the engagement levels of the user, the mobility profile and the reputation of the user. We provide an analysis of a real-world user corpus of Twitter users contributing updates to LiveDrive, a Dublin based traffic radio station.","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":"130153680","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":"Real-time Recommendation of Streamed Data","authors":"F. Hopfgartner, B. Kille, Tobias Heintz, R. Turrin","doi":"10.1145/2792838.2792839","DOIUrl":"https://doi.org/10.1145/2792838.2792839","url":null,"abstract":"This tutorial addressed two trending topics in the field of recommender systems research, namely A/B testing and real-time recommendations of streamed data. Focusing on the news domain, participants learned how to benchmark the performance of stream-based recommendation algorithms in a live recommender system and in a simulated environment.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"18 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":"121712495","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":"Adapting Recommendations to Contextual Changes Using Hierarchical Hidden Markov Models","authors":"Mehdi Hosseinzadeh Aghdam, N. Hariri, B. Mobasher, R. Burke","doi":"10.1145/2792838.2799684","DOIUrl":"https://doi.org/10.1145/2792838.2799684","url":null,"abstract":"Recommender systems help users find items of interest by tailoring their recommendations to users' personal preferences. The utility of an item for a user, however, may vary greatly depending on that user's specific situation or the context in which the item is used. Without considering these changes in preferences, the recommendations may match the general preferences of a user, but they may have small value for the user in his/her current situation. In this paper, we introduce a hierarchical hidden Markov model for capturing changes in user's preferences. Using a user's feedback sequence on items, we model the user as a hierarchical hidden Markov process and the current context of the user as a hidden variable in this model. For a given user, our model is used to infer the maximum likelihood sequence of transitions between contextual states and to predict the probability distribution for the context of the next action. The predicted context is then used to generate recommendations. Our evaluation results using Last.fm music playlist data, indicate that this approach achieves significantly better performance in terms of accuracy and diversity compared to baseline methods.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"35 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":"125826583","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}