Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization最新文献_第8页

Improving Cold Start Recommendation by Mapping Feature-Based Preferences to Item Comparisons 通过将基于特征的偏好映射到项目比较来改进冷启动推荐

Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization Pub Date : 2017-07-09 DOI: 10.1145/3079628.3079696

Saikishore Kalloori, F. Ricci

引用次数: 10

Analyzing the Impact of Social Connections on Rating Behavior in Social Recommender Systems 社会关系对社会推荐系统中评分行为的影响分析

Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization Pub Date : 2017-07-09 DOI: 10.1145/3079628.3079706

Carine Pierrette Mukamakuza

引用次数: 3

Weighted Random Walk Sampling for Multi-Relational Recommendation 多关系推荐的加权随机漫步抽样

Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization Pub Date : 2017-02-28 DOI: 10.1145/3079628.3079685

Fatemeh Vahedian, R. Burke, B. Mobasher

{"title":"Weighted Random Walk Sampling for Multi-Relational Recommendation","authors":"Fatemeh Vahedian, R. Burke, B. Mobasher","doi":"10.1145/3079628.3079685","DOIUrl":"https://doi.org/10.1145/3079628.3079685","url":null,"abstract":"In the information overloaded web, personalized recommender systems are essential tools to help users find most relevant information. The most heavily-used recommendation frameworks assume user interactions that are characterized by a single relation. However, for many tasks, such as recommendation in social networks, user-item interactions must be modeled as a complex network of multiple relations, not only a single relation. Recently research on multi-relational factorization and hybrid recommender models has shown that using extended meta-paths to capture additional information about both users and items in the network can enhance the accuracy of recommendations in such networks. Most of this work is focused on unweighted heterogeneous networks, and to apply these techniques, weighted relations must be simplified into binary ones. However, information associated with weighted edges, such as user ratings, which may be crucial for recommendation, are lost in such binarization. In this paper, we explore a random walk sampling method in which the frequency of edge sampling is a function of edge weight, and apply this generate extended meta-paths in weighted heterogeneous networks. With this sampling technique, we demonstrate improved performance on multiple data sets both in terms of recommendation accuracy and model generation efficiency.","PeriodicalId":216017,"journal":{"name":"Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114262013","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}

引用次数: 16

Probabilistic Perspectives on Collecting Human Uncertainty in Predictive Data Mining 预测数据挖掘中人类不确定性收集的概率视角

Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization Pub Date : 2017-02-28 DOI: 10.1145/3079628.3079675

Kevin Jasberg, Sergej Sizov

{"title":"Probabilistic Perspectives on Collecting Human Uncertainty in Predictive Data Mining","authors":"Kevin Jasberg, Sergej Sizov","doi":"10.1145/3079628.3079675","DOIUrl":"https://doi.org/10.1145/3079628.3079675","url":null,"abstract":"In many areas of data mining, data is collected from human beings. In this contribution, we ask the question of how people actually respond to ordinal scales. The main problem observed is that users tend to be volatile in their choices, i.e. complex cognitions do not always lead to the same decisions, but to distributions of possible decision outputs. This human uncertainty may sometimes have quite an impact on common data mining approaches and thus, the question of effective modelling this so called human uncertainty emerges naturally. Our contribution introduces two different approaches for modelling the human uncertainty of user responses. In doing so, we develop techniques in order to measure this uncertainty at the level of user inputs as well as the level of user cognition. With support of comprehensive user experiments and large-scale simulations, we systematically compare both methodologies along with their implications for personalisation approaches. Our findings demonstrate that significant amounts of users do submit something completely different (action) than they really have in mind (cognition). Moreover, we demonstrate that statistically sound evidence with respect to algorithm assessment becomes quite hard to realise, especially when explicit rankings shall be built.","PeriodicalId":216017,"journal":{"name":"Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129280612","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}

引用次数: 5

Interactive Prior Elicitation of Feature Similarities for Small Sample Size Prediction 小样本量预测中特征相似性的交互式先验启发

Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization Pub Date : 2016-12-08 DOI: 10.1145/3079628.3079698

Homayun Afrabandpey, Tomi Peltola, Samuel Kaski

{"title":"Interactive Prior Elicitation of Feature Similarities for Small Sample Size Prediction","authors":"Homayun Afrabandpey, Tomi Peltola, Samuel Kaski","doi":"10.1145/3079628.3079698","DOIUrl":"https://doi.org/10.1145/3079628.3079698","url":null,"abstract":"Regression under the \"small n$, large p\" condition, of small sample size n and large number of features p in the learning data set, is a recurring setting in which learning from data is difficult. With prior knowledge about relationships of the features, p can effectively be reduced, but explicating such prior knowledge is difficult for experts. In this paper we introduce a new method for eliciting expert prior knowledge about the similarity of the roles of features in the prediction task. The key idea is to use an interactive multidimensional-scaling (MDS) type scatterplot display of the features to elicit the similarity relationships, and then use the elicited relationships in the prior distribution of prediction parameters. Specifically, for learning to predict a target variable with Bayesian linear regression, the feature relationships are used to construct a Gaussian prior with a full covariance matrix for the regression coefficients. Evaluation of our method in experiments with simulated and real users on text data confirm that prior elicitation of feature similarities improves prediction accuracy. Furthermore, elicitation with an interactive scatterplot display outperforms straightforward elicitation where the users choose feature pairs from a feature list.","PeriodicalId":216017,"journal":{"name":"Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114948015","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}

引用次数: 11