Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization最新文献

{"title":"Modeling Individual Users' Responsiveness to Maximize Recommendation Impact","authors":"Masahiro Sato, Hidetaka Izumo, Takashi Sonoda","doi":"10.1145/2930238.2930259","DOIUrl":"https://doi.org/10.1145/2930238.2930259","url":null,"abstract":"Recommender systems provide personalized information based on a user's preferences. Differences in preferences among users are estimated from past records such as click logs or purchase logs. Recommender systems typically assume that users will respond to recommendations, provided that their favorite items are correctly selected. However, the responsiveness to recommendations depends on the type of users; while some users might be easily persuaded to take action, others might be more hesitant. In this paper, we propose a purchase prediction model that incorporates the differences in the responsiveness. We derived the individual users' responsiveness from a combination of purchase logs and recommendation logs. Improvement in the accuracy of purchase prediction was verified using a grocery shopping dataset. Another relatively unexplored yet important objective of recommender algorithms is to maximize recommendation impact, which is defined as the increase in purchase probability through recommendations. The impact of recommendations by our model exceeded that of a conventional model that ignores individual users' responsiveness. These results demonstrate the importance of modeling the responsiveness of individual users. In cases where recommendation logs are insufficient, the responsiveness needs to be estimated from other sources. Consequently, we investigated the correlation of the responsiveness with user attributes and item attributes. The estimates of the responsiveness from the correlated attributes outperformed the mean estimates. Furthermore, the recommendation impact of the model estimated from the correlated attributes was almost comparable to that of the model estimated from recommendation logs. These findings can help overcome the cold-start problem of inadequate recommendation logs. Our study presents a new direction in the field of personalization based on the responsiveness to recommendations.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114727590","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":"Designing Culture-based Persuasive Technology to Promote Physical Activity among University Students","authors":"Kiemute Oyibo","doi":"10.1145/2930238.2930372","DOIUrl":"https://doi.org/10.1145/2930238.2930372","url":null,"abstract":"Overweight and obesity are taking a huge toll on nations' financial and health resources annually. Student populations are at risk due to their sedentary lifestyles and the high demands of academic scholarship, leaving them with little or no time to exercise. Recently, persuasive technology, promoting physical activity, has been proposed. However, the traditional \"one-size-fits-all\" approach has not been effective among the student population. This calls for a newer and more effective approach, which leverages the available recreational and technological resources in the university at personal, social and cultural levels. In an effort to address students' sedentary behaviors, I aim to combine user behavior models, persuasive technology design and cultural strategies from Health Sciences for a more personalized and effective intervention. This paper presents the approach and the preliminary results of two user studies among 218 and 292 subjects from a Canadian and a Nigerian university respectively.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132729709","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":"Where's Your Mind At?: Video-Based Mind Wandering Detection During Film Viewing","authors":"Angela E. B. Stewart, Nigel Bosch, Huili Chen, P. Donnelly, S. D’Mello","doi":"10.1145/2930238.2930266","DOIUrl":"https://doi.org/10.1145/2930238.2930266","url":null,"abstract":"Mind wandering (MW) is a ubiquitous phenomenon in which attention involuntarily shifts from task-related processing to task-unrelated thoughts. This study reports preliminary results of a video-based MW detector during film viewing. We collected training data in a study where participants self-reported when they caught themselves MW over the course of watching a 32.5 minute commercial film. We trained classification models on automatically extracted facial features and bodily movement and were able to detect MW with an F1 of .30. The model was successful in reproducing the MW distribution obtained from the self-reports","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132783653","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 Framework for Dynamic Knowledge Modeling in Textbook-Based Learning","authors":"Yun Huang, M. Yudelson, Shuguang Han, Daqing He, Peter Brusilovsky","doi":"10.1145/2930238.2930258","DOIUrl":"https://doi.org/10.1145/2930238.2930258","url":null,"abstract":"Various e-learning systems that provide electronic textbooks are gathering data on large numbers of student reading interactions. This data can potentially be used to model student knowledge acquisition. However, reading activity is often overlooked in canonical student modeling. Prior studies modeling learning from reading either estimate student knowledge at the end of all reading activities, or use quiz performance data with expert-crafted knowledge components (KCs). In this work, we demonstrate that the dynamic modeling of student knowledge is feasible and that automatic text analysis can be applied to save expert effort. We propose a data-driven approach for dynamic student modeling in textbook-based learning. We formulate the problem of modeling learning from reading as a reading-time prediction problem, reconstruct existing popular student models (such as Knowledge Tracing) and explore two automatic text analysis approaches (bag-of-words-based and latent semantic-based) to build the KC model. We evaluate the proposed framework using a dataset collected from a Human-Computer Interaction course. Results show that our approach for reading modeling is plausible; the proposed Knowledge Tracing-based student model reliably outperforms baselines and the latent semantic-based approach can be a promising way to construct a KC model. Serving as the first step to model dynamic knowledge in textbook-based learning, our framework can be applied to a broader context of open-corpus personalized learning.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127425255","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":"Towards Comprehensive User Modeling on the Social Web for Personalized Link Recommendations","authors":"Guangyuan Piao","doi":"10.1145/2930238.2930367","DOIUrl":"https://doi.org/10.1145/2930238.2930367","url":null,"abstract":"User modeling for individual users on the Social Web plays a significant role and is a fundamental step for personalization as well as recommendations. Previous studies have proposed various user modeling strategies in different dimensions such as (1) interest representation, (2) interest propagation, (3) content enrichment and (4) temporal dynamics of user interests. This research mainly focuses on the first two dimensions interest representation and propagation. In addition, we also investigate the combination of these four dimensions and their synergistic effect on the quality of user modeling. Different user modeling strategies will then be evaluated in the context of personalized link recommender systems using standard evaluation methodologies such as Mean Reciprocal Rank (MRR), recall (R@N) and success (S@N) at rank N.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131252256","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 Life and Times of Information in Networks","authors":"Lada A. Adamic","doi":"10.1145/2930238.2930292","DOIUrl":"https://doi.org/10.1145/2930238.2930292","url":null,"abstract":"Cascades of information-sharing are a primary mechanism by which content reaches its audience on social media. In this talk, I will describe three large-scale analyses of reshare cascades on Facebook, which were performed in aggregate using de-identified data. The first study aims to understand how predictable the growth of cascades is. We formulate the problem as one of predicting whether a cascade will double in size, and find that the prediction accuracy increases the longer a cascade has been observed. Furthermore, temporal and structural features of the cascade, as well as properties of its origin and content, along with the characteristics of those participating, are all useful in predicting how much more a cascade will grow. If we examine these cascades over significantly longer time scales, we find that many large cascades recur, exhibiting multiple bursts of popularity with periods of quiescence in between. We characterize recurrence by measuring the time elapsed between bursts, their overlap and proximity in the social network, and the diversity in the demographics of individuals participating in each peak. We discover that content virality, as revealed by its initial popularity, is a main driver of recurrence, with the availability of multiple copies of that content helping to spark new bursts. Still, beyond a certain popularity of content, the rate of recurrence drops as cascades start exhausting the population of interested individuals. We reproduce these observed patterns in a simple model of content recurrence simulated on a real social network. Using only characteristics of a cascade’s initial burst, we demonstrate strong performance in predicting whether it will recur in the future. Finally, I will discuss not just how information is transmitted perfectly, but how it evolves as changes are made as it is copied. Using a dataset of thousands of memes collectively replicated hundreds of millions of times, we find that the information undergoes an evolutionary process that exhibits several regularities. A meme’s mutation rate characterizes the population distribution of its variants, in accordance with the Yule process. Variants further apart in the diffusion cascade have greater edit distance, as would be expected in an iterative, imperfect replication process. Some text sequences can confer a replicative advantage; these sequences are abundant and transfer “laterally” between different memes. Subpopulations of the social network can preferentially transmit a specific variant of a meme if the variant matches their beliefs or culture. Understanding the mechanism driving change in diffusing information has important implications for how we interpret and harness the information that reaches us through our social networks.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124315596","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":"Modeling User Exploration and Boundary Testing in Digital Learning Games","authors":"V. E. Owen, Gabriella Anton, R. Baker","doi":"10.1145/2930238.2930271","DOIUrl":"https://doi.org/10.1145/2930238.2930271","url":null,"abstract":"Digital games can be potent problem solving environments which afford discovery learning through thoughtful exploration [1, 2]. As such, game microworlds facilitate self-regulated learning through sandbox elements in which students have agency in individualizing their pathways of interaction [3]. These agency-driven environments can support learning via individual discovery of problem space constraints and solutions, particularly through boundary testing and productive failure [cf. 4]. Thus, modeling of user interaction in digital learning games can provide considerable insight into emergent trajectories of discovery-based progression, in which equally engaged players may interact differently with the system. To this end, this research leverages educational data mining (EDM) [5] to investigate organic player trajectories of thoughtful exploration (around boundary testing and productive failure) in a learning gamespace. We align behavioral coding with log file data to automatically detect sequences of thoughtful exploration (TE) in play. Results include a robust predictive model of event-stream TE, with multiple trajectories of emergent student behavior-offering insight into organic learning pathways through the game-based problem space, and informing iterative design in optimization of user experience and student engagement.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117304421","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":"Modelling User Collaboration in Social Networks Using Edits and Comments","authors":"I. Adaji, Julita Vassileva","doi":"10.1145/2930238.2930289","DOIUrl":"https://doi.org/10.1145/2930238.2930289","url":null,"abstract":"Research has shown that in Q&A social networks, collaboration between respondents results in quality answers. Since good answers are required to keep any Q&A social network active, it is important to understand the characteristics of these collaborations and the collaborators. In this paper, we investigate how Stack Overflow promotes collaboration by allowing users to edit existing questions and answers in order to improve them. Using over 40,000 answer posts, our study reveals that collaboration in answer posts is not a function of achievement earned in terms of badges, as most edits associated with \"best answer\" rewards were posted by users who have not earned any answer badge. Our study further shows that posts that earned the \"best answer\" reward have more comments than those that did not. This study though, work in progress, can aid developers in implementing collaboration strategies in social networks that work.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123185556","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":"TBPR","authors":"Huihuai Qiu, G. Guo, J. Zhang, Zhu Sun, H. Nguyen, Yun Liu","doi":"10.1145/2930238.2930272","DOIUrl":"https://doi.org/10.1145/2930238.2930272","url":null,"abstract":"In e-commerce systems, user preference can be inferred from multivariate implicit feedback (i.e., actions). However, most methods merely focus on homogeneous implicit feedback (i.e., purchase). In this paper, we adopt another two typical actions, i.e., view and like, as auxiliaries to enhance purchase recommendation, whereby a trinity Bayesian personalized ranking (TBPR) method is proposed. Specifically, we introduce trinity preference to investigate the difference of users' preference among three types of items: 1) items with purchase action; 2) items with only auxiliary actions; 3) items without any action. Empirical study on the real-world dataset demonstrates that our method significantly outperforms state-of-the-art algorithms.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123198471","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":"Effect of Different Implicit Social Networks on Recommending Research Papers","authors":"Shaikhah Alotaibi, Julita Vassileva","doi":"10.1145/2930238.2930293","DOIUrl":"https://doi.org/10.1145/2930238.2930293","url":null,"abstract":"Combining social network information with collaborative filtering recommendation algorithms has successfully reduced some of the drawbacks of collaborative filtering and increased the accuracy of recommendations. However, all approaches in the domain of research paper recommendation have used explicit social relations that users have initiated which has the problem of low recommendation coverage. We argued that the available data in social bookmarking Web sites such as CiteULike or Mendeley could be exploited to connect similar users using implicit social connections based on their bookmarking behavior. In this paper, we proposed three different implicit social networks-readership, co-readership, and tag-based and we compared the recommendation accuracy of several recommendation algorithms using data from the proposed social networks as input to the recommendation algorithms. Then, we tested which implicit social network provides the best recommendation accuracy. We found that, for the most part, the social recommender is the best algorithm and that the readership network with reciprocal social relations provides the best information source for recommendations but with low coverage. However, the co-readership network provide good recommendation accuracy and better user coverage of recommendation.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"276 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122704137","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}