{"title":"Concept Graph Learning from Educational Data","authors":"Yiming Yang, Hanxiao Liu, J. Carbonell, Wanli Ma","doi":"10.1145/2684822.2685292","DOIUrl":"https://doi.org/10.1145/2684822.2685292","url":null,"abstract":"This paper addresses an open challenge in educational data mining, i.e., the problem of using observed prerequisite relations among courses to learn a directed universal concept graph, and using the induced graph to predict unobserved prerequisite relations among a broader range of courses. This is particularly useful to induce prerequisite relations among courses from different providers (universities, MOOCs, etc.). We propose a new framework for inference within and across two graphs---at the course level and at the induced concept level---which we call Concept Graph Learning (CGL). In the training phase, our system projects the course-level links onto the concept space to induce directed concept links; in the testing phase, the concept links are used to predict (unobserved) prerequisite links for test-set courses within the same institution or across institutions. The dual mappings enable our system to perform an interlingua-style transfer learning, e.g. treating the concept graph as the interlingua, and inducing prerequisite links in a transferable manner across different universities. Experiments on our newly collected data sets of courses from MIT, Caltech, Princeton and CMU show promising results, including the viability of CGL for transfer learning.","PeriodicalId":179443,"journal":{"name":"Proceedings of the Eighth ACM International Conference on Web Search and Data Mining","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126581546","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":"New Directions in Recommender Systems","authors":"J. Leskovec","doi":"10.1145/2684822.2697044","DOIUrl":"https://doi.org/10.1145/2684822.2697044","url":null,"abstract":"Recommender systems are an integral part of how we experience the Web today and they have become so ubiquitous that we do not even notice them anymore. However, today's recommender systems mostly treat items they recommend as black boxes and primarily focus on extracting correlations and co-counts from user behavior data. In this talk I argue that next generation recommender systems will require deep understanding of items being recommended as well as modeling the relationships between those items. I will present examples how auxiliary data about items (descriptions, reviews, product specifications) can be used to improve recommendations.","PeriodicalId":179443,"journal":{"name":"Proceedings of the Eighth ACM International Conference on Web Search and Data Mining","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133061822","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":"Session details: Session 7: User Modeling, Mobility, and Recommendations","authors":"Grace Hui Yang","doi":"10.1145/3251099","DOIUrl":"https://doi.org/10.1145/3251099","url":null,"abstract":"","PeriodicalId":179443,"journal":{"name":"Proceedings of the Eighth ACM International Conference on Web Search and Data Mining","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132181421","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":"Session details: Session 2: Web Search","authors":"M. de Rijke","doi":"10.1145/3251092","DOIUrl":"https://doi.org/10.1145/3251092","url":null,"abstract":"","PeriodicalId":179443,"journal":{"name":"Proceedings of the Eighth ACM International Conference on Web Search and Data Mining","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122517346","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}
Komal Kapoor, Karthik Subbian, J. Srivastava, P. Schrater
{"title":"Just in Time Recommendations: Modeling the Dynamics of Boredom in Activity Streams","authors":"Komal Kapoor, Karthik Subbian, J. Srivastava, P. Schrater","doi":"10.1145/2684822.2685306","DOIUrl":"https://doi.org/10.1145/2684822.2685306","url":null,"abstract":"Recommendation methods have mainly dealt with the problem of recommending new items to the user while user visitation behavior to the familiar items (items which have been consumed before) are little understood. In this paper, we analyze user activity streams and show that user's temporal consumption of familiar items is driven by boredom. Specifically, users move on to a different item when bored and return to the same item when their interest is restored. To model this behavior we include two latent psychological states of preference for items - sensitization and boredom. In the sensitization state the user is highly engaged with the item, while in the boredom state the user is disinterested. We model this behavior using a Hidden Semi-Markov Model for the gaps between user consumption activities. We show that our model performs much better than the state-of-the-art temporal recommendation models at predicting the revisit time to the item. Moreover, we attribute two main reasons for this: (1) recommending items that are not in the bored state for the user, (2) recommending items where user has restored her interests.","PeriodicalId":179443,"journal":{"name":"Proceedings of the Eighth ACM International Conference on Web Search and Data Mining","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132725703","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}
B. Liu, Deguang Kong, Lei Cen, N. Gong, Hongxia Jin, Hui Xiong
{"title":"Personalized Mobile App Recommendation: Reconciling App Functionality and User Privacy Preference","authors":"B. Liu, Deguang Kong, Lei Cen, N. Gong, Hongxia Jin, Hui Xiong","doi":"10.1145/2684822.2685322","DOIUrl":"https://doi.org/10.1145/2684822.2685322","url":null,"abstract":"Recent years have witnessed a rapid adoption of mobile devices and a dramatic proliferation of mobile applications (Apps for brevity). However, the large number of mobile Apps makes it difficult for users to locate relevant Apps. Therefore, recommending Apps becomes an urgent task. Traditional recommendation approaches focus on learning the interest of a user and the functionality of an item (e.g., an App) from a set of user-item ratings, and they recommend an item to a user if the item's functionality well matches the user's interest. However, Apps could have privileges to access a user's sensitive resources ( e.g., contact, message, and location). As a result, a user chooses an App not only because of its functionality, but also because it respects the user's privacy preference. To the best of our knowledge, this paper presents the first systematic study on incorporating both interest-functionality interactions and users' privacy preferences to perform personalized App recommendations. Specifically, we first construct a new model to capture the trade-off between functionality and user privacy preference. Then we crawled a real-world dataset (16,344 users, 6,157 Apps, and 263,054 ratings) from Google Play and use it to comprehensively evaluate our model and previous methods. We find that our method consistently and substantially outperforms the state-of-the-art approaches, which implies the importance of user privacy preference on personalized App recommendations. Moreover, we explore the impact of different levels of privacy information on the performances of our method, which gives us insights on what resources are more likely to be treated as private by users and influence users' behaviors at selecting Apps.","PeriodicalId":179443,"journal":{"name":"Proceedings of the Eighth ACM International Conference on Web Search and Data Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129871317","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":"Session details: Session 6: Crowdsourcing, Temporal and Location-based mining","authors":"C. Clarke","doi":"10.1145/3251097","DOIUrl":"https://doi.org/10.1145/3251097","url":null,"abstract":"","PeriodicalId":179443,"journal":{"name":"Proceedings of the Eighth ACM International Conference on Web Search and Data Mining","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133768575","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}
Saehoon Kim, Yuxiong He, Seung-won Hwang, S. Elnikety, Seungjin Choi
{"title":"Delayed-Dynamic-Selective (DDS) Prediction for Reducing Extreme Tail Latency in Web Search","authors":"Saehoon Kim, Yuxiong He, Seung-won Hwang, S. Elnikety, Seungjin Choi","doi":"10.1145/2684822.2685289","DOIUrl":"https://doi.org/10.1145/2684822.2685289","url":null,"abstract":"A commercial web search engine shards its index among many servers, and therefore the response time of a search query is dominated by the slowest server that processes the query. Prior approaches target improving responsiveness by reducing the tail latency of an individual search server. They predict query execution time, and if a query is predicted to be long-running, it runs in parallel, otherwise it runs sequentially. These approaches are, however, not accurate enough for reducing a high tail latency when responses are aggregated from many servers because this requires each server to reduce a substantially higher tail latency (e.g., the 99.99th-percentile), which we call extreme tail latency. We propose a prediction framework to reduce the extreme tail latency of search servers. The framework has a unique set of characteristics to predict long-running queries with high recall and improved precision. Specifically, prediction is delayed by a short duration to allow many short-running queries to complete without parallelization, and to allow the predictor to collect a set of dynamic features using runtime information. These features estimate query execution time with high accuracy. We also use them to estimate the prediction errors to override an uncertain prediction by selectively accelerating the query for a higher recall. We evaluate the proposed prediction framework to improve search engine performance in two scenarios using a simulation study: (1) query parallelization on a multicore processor, and (2) query scheduling on a heterogeneous processor. The results show that, for both scenarios, the proposed framework is effective in reducing the extreme tail latency compared to a start-of-the-art predictor because of its higher recall, and it improves server throughput by more than 70% because of its improved precision.","PeriodicalId":179443,"journal":{"name":"Proceedings of the Eighth ACM International Conference on Web Search and Data Mining","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133608030","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":"You Are Where You Go: Inferring Demographic Attributes from Location Check-ins","authors":"Yuan Zhong, Nicholas Jing Yuan, Wen Zhong, Fuzheng Zhang, Xing Xie","doi":"10.1145/2684822.2685287","DOIUrl":"https://doi.org/10.1145/2684822.2685287","url":null,"abstract":"User profiling is crucial to many online services. Several recent studies suggest that demographic attributes are predictable from different online behavioral data, such as users' \"Likes\" on Facebook, friendship relations, and the linguistic characteristics of tweets. But location check-ins, as a bridge of users' offline and online lives, have by and large been overlooked in inferring user profiles. In this paper, we investigate the predictive power of location check-ins for inferring users' demographics and propose a simple yet general location to profile (L2P) framework. More specifically, we extract rich semantics of users' check-ins in terms of spatiality, temporality, and location knowledge, where the location knowledge is enriched with semantics mined from heterogeneous domains including both online customer review sites and social networks. Additionally, tensor factorization is employed to draw out low dimensional representations of users' intrinsic check-in preferences considering the above factors. Meanwhile, the extracted features are used to train predictive models for inferring various demographic attributes. We collect a large dataset consisting of profiles of 159,530 verified users from an online social network. Extensive experimental results based upon this dataset validate that: 1) Location check-ins are diagnostic representations of a variety of demographic attributes, such as gender, age, education background, and marital status; 2) The proposed framework substantially outperforms compared models for profile inference in terms of various evaluation metrics, such as precision, recall, F-measure, and AUC.","PeriodicalId":179443,"journal":{"name":"Proceedings of the Eighth ACM International Conference on Web Search and Data Mining","volume":"54 98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130908630","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":"Diluted Treatment Effect Estimation for Trigger Analysis in Online Controlled Experiments","authors":"Alex Deng, Victor Hu","doi":"10.1145/2684822.2685307","DOIUrl":"https://doi.org/10.1145/2684822.2685307","url":null,"abstract":"Online controlled experiments, also called A/B testing, is playing a central role in many data-driven web-facing companies. It is well known and intuitively obvious to many practitioners that when testing a feature with low coverage, analyzing all data collected without zooming into the part that could be affected by the treatment often leads to under-powered hypothesis testing. A common practice is to use triggered analysis. To estimate the overall treatment effect, certain dilution formula is then applied to translate the estimated effect in triggered analysis back to the original all up population. In this paper, we discuss two different types of trigger analyses. We derive correct dilution formulas and show for a set of widely used metrics, namely ratio metrics, correctly deriving and applying those dilution formulas are not trivial. We observe many practitioners in this industry are often applying approximate formulas or even wrong formulas when doing effect dilution calculation. To deal with that, instead of estimating trigger treatment effect followed by effect translation using dilution formula, we aim at combining these two steps into one streamlined analysis, producing more accurate estimation of overall treatment effect together with even higher statistical power than a triggered analysis. The approach we propose in this paper is intuitive, easy to apply and general enough for all types of triggered analyses and all types of metrics.","PeriodicalId":179443,"journal":{"name":"Proceedings of the Eighth ACM International Conference on Web Search and Data Mining","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133354608","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}