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"dateAdded": "2016-04-20T01:55:37Z", "dateModified": "2016-04-20T01:55:37Z" } }, { "key": "6D78TWTQ", "version": 7251, "library": { "type": "group", "id": 33154, "name": "UMUAI", "links": { "alternate": { "href": "https://www.zotero.org/groups/umuai", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/33154/items/6D78TWTQ", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/umuai/items/6D78TWTQ", "type": "text/html" } }, "meta": { "createdByUser": { "id": 450058, "username": "alfredkobsa", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/alfredkobsa", "type": "text/html" } } }, "creatorSummary": "Bixler and D’Mello", "parsedDate": "2015-09-27", "numChildren": 2 }, "data": { "key": "6D78TWTQ", "version": 7251, "itemType": "journalArticle", "title": "Automatic gaze-based user-independent detection of mind wandering during computerized reading", "creators": [ { "creatorType": "author", "firstName": "Robert", "lastName": "Bixler" }, { "creatorType": "author", "firstName": "Sidney", "lastName": "D’Mello" } ], "abstractNote": "Mind wandering is a ubiquitous phenomenon where attention involuntarily shifts from task-related thoughts to internal task-unrelated thoughts. Mind wandering can have negative effects on performance; hence, intelligent interfaces that detect mind wandering can improve performance by intervening and restoring attention to the current task. We investigated the use of eye gaze and contextual cues to automatically detect mind wandering during reading with a computer interface. Participants were pseudorandomly probed to report mind wandering while an eye tracker recorded their gaze during the reading task. Supervised machine learning techniques detected positive responses to mind wandering probes from eye gaze and context features in a user-independent fashion. Mind wandering was detected with an accuracy of 72 % (expected accuracy by chance was 60 %) when probed at the end of a page and an accuracy of 67 % (chance was 59 %) when probed in the midst of reading a page. Global gaze features (gaze patterns independent of content, such as fixation durations) were more effective than content-specific local gaze features. An analysis of the features revealed diagnostic patterns of eye gaze behavior during mind wandering: (1) certain types of fixations were longer; (2) reading times were longer than expected; (3) more words were skipped; and (4) there was a larger variability in pupil diameter. Finally, the automatically detected mind wandering rate correlated negatively with measures of learning and transfer even after controlling for prior knowledge, thereby providing evidence of predictive validity. Possible improvements to the detector and applications that utilize the detector are discussed.", "publicationTitle": "User Modeling and User-Adapted Interaction", "publisher": "", "place": "", "date": "2015/09/27", "volume": "26", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "33-68", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "User Model User-Adap Inter", "DOI": "10.1007/s11257-015-9167-1", "citationKey": "", "url": "http://link.springer.com/article/10.1007/s11257-015-9167-1", "accessDate": "2016-04-20T01:55:37Z", "PMID": "", "PMCID": "", "ISSN": "0924-1868, 1573-1391", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "link.springer.com", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Gaze tracking", "type": 1 }, { "tag": "Management of Computing and Information Systems", "type": 1 }, { "tag": "Mind wandering", "type": 1 }, { "tag": "Multimedia Information Systems", "type": 1 }, { "tag": "User Interfaces and Human Computer Interaction", "type": 1 }, { "tag": "User modeling", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2016-04-20T01:55:37Z", "dateModified": "2016-04-20T01:55:37Z" } }, { "key": "GFHJDVKZ", "version": 7251, "library": { "type": "group", "id": 33154, "name": "UMUAI", "links": { "alternate": { "href": "https://www.zotero.org/groups/umuai", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/33154/items/GFHJDVKZ", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/umuai/items/GFHJDVKZ", "type": "text/html" } }, "meta": { "createdByUser": { "id": 450058, "username": "alfredkobsa", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/alfredkobsa", "type": "text/html" } } }, "creatorSummary": "Codina et al.", "parsedDate": "2015-03-31", "numChildren": 2 }, "data": { "key": "GFHJDVKZ", "version": 7251, "itemType": "journalArticle", "title": "Distributional semantic pre-filtering in context-aware recommender systems", "creators": [ { "creatorType": "author", "firstName": "Victor", "lastName": "Codina" }, { "creatorType": "author", "firstName": "Francesco", "lastName": "Ricci" }, { "creatorType": "author", "firstName": "Luigi", "lastName": "Ceccaroni" } ], "abstractNote": "Context-aware recommender systems improve context-free recommenders by exploiting the knowledge of the contextual situation under which a user experienced and rated an item. They use data sets of contextually-tagged ratings to predict how the target user would evaluate (rate) an item in a given contextual situation, with the ultimate goal to recommend the items with the best estimated ratings. This paper describes and evaluates a pre-filtering approach to context-aware recommendation, called distributional-semantics pre-filtering (DSPF), which exploits in a novel way the distributional semantics of contextual conditions to build more precise context-aware rating prediction models. In DSPF, given a target contextual situation (of a target user), a matrix-factorization predictive model is built by using the ratings tagged with the contextual situations most similar to the target one. Then, this model is used to compute rating predictions and identify recommendations for that specific target contextual situation. In the proposed approach, the definition of the similarity of contextual situations is based on the distributional semantics of their composing conditions: situations are similar if they influence the user’s ratings in a similar way. This notion of similarity has the advantage of being directly derived from the rating data; hence it does not require a context taxonomy. We analyze the effectiveness of DSPF varying the specific method used to compute the situation-to-situation similarity. We also show how DSPF can be further improved by using clustering techniques. Finally, we evaluate DSPF on several contextually-tagged data sets and demonstrate that it outperforms state-of-the-art context-aware approaches.", "publicationTitle": "User Modeling and User-Adapted Interaction", "publisher": "", "place": "", "date": "2015/03/31", "volume": "26", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "1-32", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "User Model User-Adap Inter", "DOI": "10.1007/s11257-015-9158-2", "citationKey": "", "url": "http://link.springer.com/article/10.1007/s11257-015-9158-2", "accessDate": "2016-04-20T01:55:37Z", "PMID": "", "PMCID": "", "ISSN": "0924-1868, 1573-1391", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "link.springer.com", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Clustering", "type": 1 }, { "tag": "Collaborative filtering", "type": 1 }, { "tag": "Context-awareness", "type": 1 }, { "tag": "Distributional semantics", "type": 1 }, { "tag": "Management of Computing and Information Systems", "type": 1 }, { "tag": "Matrix factorization", "type": 1 }, { "tag": "Multimedia Information Systems", "type": 1 }, { "tag": "Pre-filtering", "type": 1 }, { "tag": "Recommender systems", "type": 1 }, { "tag": "User Interfaces and Human Computer Interaction", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2016-04-20T01:55:37Z", "dateModified": "2016-04-20T01:55:37Z" } }, { "key": "PI6ZQX5A", "version": 7251, "library": { "type": "group", "id": 33154, "name": "UMUAI", "links": { "alternate": { "href": "https://www.zotero.org/groups/umuai", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/33154/items/PI6ZQX5A", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/umuai/items/PI6ZQX5A", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/33154/items/6D78TWTQ", "type": "application/json" } 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}, { "key": "CSZSTC67", "version": 7251, "library": { "type": "group", "id": 33154, "name": "UMUAI", "links": { "alternate": { "href": "https://www.zotero.org/groups/umuai", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/33154/items/CSZSTC67", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/umuai/items/CSZSTC67", "type": "text/html" } }, "meta": { "createdByUser": { "id": 450058, "username": "alfredkobsa", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/alfredkobsa", "type": "text/html" } } }, "creatorSummary": "Beel et al.", "parsedDate": "2016-03-12", "numChildren": 2 }, "data": { "key": "CSZSTC67", "version": 7251, "itemType": "journalArticle", "title": "Towards reproducibility in recommender-systems research", "creators": [ { "creatorType": "author", "firstName": "Joeran", "lastName": "Beel" }, { "creatorType": "author", "firstName": "Corinna", "lastName": "Breitinger" }, { "creatorType": "author", "firstName": "Stefan", "lastName": "Langer" }, { "creatorType": "author", "firstName": "Andreas", "lastName": "Lommatzsch" }, { "creatorType": "author", "firstName": "Bela", "lastName": "Gipp" } ], "abstractNote": "Numerous recommendation approaches are in use today. However, comparing their effectiveness is a challenging task because evaluation results are rarely reproducible. In this article, we examine the challenge of reproducibility in recommender-system research. We conduct experiments using Plista’s news recommender system, and Docear’s research-paper recommender system. The experiments show that there are large discrepancies in the effectiveness of identical recommendation approaches in only slightly different scenarios, as well as large discrepancies for slightly different approaches in identical scenarios. For example, in one news-recommendation scenario, the performance of a content-based filtering approach was twice as high as the second-best approach, while in another scenario the same content-based filtering approach was the worst performing approach. We found several determinants that may contribute to the large discrepancies observed in recommendation effectiveness. Determinants we examined include user characteristics (gender and age), datasets, weighting schemes, the time at which recommendations were shown, and user-model size. Some of the determinants have interdependencies. For instance, the optimal size of an algorithms’ user model depended on users’ age. Since minor variations in approaches and scenarios can lead to significant changes in a recommendation approach’s performance, ensuring reproducibility of experimental results is difficult. We discuss these findings and conclude that to ensure reproducibility, the recommender-system community needs to (1) survey other research fields and learn from them, (2) find a common understanding of reproducibility, (3) identify and understand the determinants that affect reproducibility, (4) conduct more comprehensive experiments, (5) modernize publication practices, (6) foster the development and use of recommendation frameworks, and (7) establish best-practice guidelines for recommender-systems research.", "publicationTitle": "User Modeling and User-Adapted Interaction", "publisher": "", "place": "", "date": "2016/03/12", "volume": "26", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "69-101", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "User Model User-Adap Inter", "DOI": "10.1007/s11257-016-9174-x", "citationKey": "", "url": "http://link.springer.com/article/10.1007/s11257-016-9174-x", "accessDate": "2016-04-20T01:55:37Z", "PMID": "", "PMCID": "", "ISSN": "0924-1868, 1573-1391", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "link.springer.com", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Evaluation", "type": 1 }, { "tag": "Experimentation", "type": 1 }, { "tag": "Management of Computing and Information Systems", "type": 1 }, { "tag": "Multimedia Information Systems", "type": 1 }, { "tag": "Recommender systems", "type": 1 }, { "tag": "Reproducibility", "type": 1 }, { "tag": "User Interfaces and Human Computer Interaction", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2016-04-20T01:55:37Z", "dateModified": "2016-04-20T01:55:37Z" } }, { "key": "6HAKB59I", "version": 7250, "library": { "type": "group", "id": 33154, "name": "UMUAI", "links": { "alternate": { "href": "https://www.zotero.org/groups/umuai", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/33154/items/6HAKB59I", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/umuai/items/6HAKB59I", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/33154/items/4ATQRWJ2", "type": "application/json" } }, "meta": { "createdByUser": { "id": 450058, "username": "alfredkobsa", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/alfredkobsa", "type": "text/html" } } } }, "data": { "key": "6HAKB59I", "version": 7250, "parentItem": "4ATQRWJ2", "itemType": "attachment", "linkMode": "linked_url", "title": "Full Text PDF", "accessDate": "2016-04-20T01:55:10Z", "url": "http://link.springer.com/content/pdf/10.1007%2Fs11257-015-9165-3.pdf", "note": "", "contentType": "application/pdf", "charset": "", "tags": [], "relations": {}, "dateAdded": "2016-04-20T01:55:10Z", "dateModified": "2016-04-20T01:55:10Z" } }, { "key": "4ATQRWJ2", "version": 7250, "library": { "type": "group", "id": 33154, "name": "UMUAI", "links": { "alternate": { "href": "https://www.zotero.org/groups/umuai", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/33154/items/4ATQRWJ2", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/umuai/items/4ATQRWJ2", "type": "text/html" } }, "meta": { "createdByUser": { "id": 450058, "username": "alfredkobsa", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/alfredkobsa", "type": "text/html" } } }, "creatorSummary": "Jannach et al.", "parsedDate": "2015-07-25", "numChildren": 2 }, "data": { "key": "4ATQRWJ2", "version": 7250, "itemType": "journalArticle", "title": "What recommenders recommend: an analysis of recommendation biases and possible countermeasures", "creators": [ { "creatorType": "author", "firstName": "Dietmar", "lastName": "Jannach" }, { "creatorType": "author", "firstName": "Lukas", "lastName": "Lerche" }, { "creatorType": "author", "firstName": "Iman", "lastName": "Kamehkhosh" }, { "creatorType": "author", "firstName": "Michael", "lastName": "Jugovac" } ], "abstractNote": "Most real-world recommender systems are deployed in a commercial context or designed to represent a value-adding service, e.g., on shopping or Social Web platforms, and typical success indicators for such systems include conversion rates, customer loyalty or sales numbers. In academic research, in contrast, the evaluation and comparison of different recommendation algorithms is mostly based on offline experimental designs and accuracy or rank measures which are used as proxies to assess an algorithm’s recommendation quality. In this paper, we show that popular recommendation techniques—despite often being similar when compared with the help of accuracy measures—can be quite different with respect to which items they recommend. We report the results of an in-depth analysis in which we compare several recommendations strategies from different perspectives, including accuracy, catalog coverage and their bias to recommend popular items. Our analyses reveal that some recent techniques that perform well with respect to accuracy measures focus their recommendations on a tiny fraction of the item spectrum or recommend mostly top sellers. We analyze the reasons for some of these biases in terms of algorithmic design and parameterization and show how the characteristics of the recommendations can be altered by hyperparameter tuning. Finally, we propose two novel algorithmic schemes to counter these popularity biases.", "publicationTitle": "User Modeling and User-Adapted Interaction", "publisher": "", "place": "", "date": "2015/07/25", "volume": "25", "issue": "5", "section": "", "partNumber": "", "partTitle": "", "pages": "427-491", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "User Model User-Adap Inter", "DOI": "10.1007/s11257-015-9165-3", "citationKey": "", "url": "http://link.springer.com/article/10.1007/s11257-015-9165-3", "accessDate": "2016-04-20T01:55:10Z", "PMID": "", "PMCID": "", "ISSN": "0924-1868, 1573-1391", "archive": "", "archiveLocation": "", "shortTitle": "What recommenders recommend", "language": "en", "libraryCatalog": "link.springer.com", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Bias", "type": 1 }, { "tag": "Evaluation", "type": 1 }, { "tag": "Management of Computing and Information Systems", "type": 1 }, { "tag": "Multimedia Information 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"application/json" } }, "meta": { "createdByUser": { "id": 450058, "username": "alfredkobsa", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/alfredkobsa", "type": "text/html" } } } }, "data": { "key": "5VDEWPMA", "version": 7249, "parentItem": "PT87T43G", "itemType": "attachment", "linkMode": "linked_url", "title": "Full Text PDF", "accessDate": "2016-04-20T01:54:39Z", "url": "http://link.springer.com/content/pdf/10.1007%2Fs11257-015-9166-2.pdf", "note": "", "contentType": "application/pdf", "charset": "", "tags": [], "relations": {}, "dateAdded": "2016-04-20T01:54:39Z", "dateModified": "2016-04-20T01:54:39Z" } }, { "key": "S72W7Z84", "version": 7249, "library": { "type": "group", "id": 33154, "name": "UMUAI", "links": { "alternate": { "href": "https://www.zotero.org/groups/umuai", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/33154/items/S72W7Z84", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/umuai/items/S72W7Z84", "type": "text/html" } }, "meta": { "createdByUser": { "id": 450058, "username": "alfredkobsa", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/alfredkobsa", "type": "text/html" } } }, "creatorSummary": "Sosnovsky and Brusilovsky", "parsedDate": "2015-07-10", "numChildren": 2 }, "data": { "key": "S72W7Z84", "version": 7249, "itemType": "journalArticle", "title": "Evaluation of topic-based adaptation and student modeling in QuizGuide", "creators": [ { "creatorType": "author", "firstName": "Sergey", "lastName": "Sosnovsky" }, { "creatorType": "author", "firstName": "Peter", "lastName": "Brusilovsky" } ], "abstractNote": "This paper presents an in-depth analysis of a nonconventional topic-based personalization approach for adaptive educational systems (AES) that we have explored for a number of years in the context of university programming courses. With this approach both student modeling and adaptation are based on coarse-grained knowledge units that we called topics. Our motivation for the topic-based personalization was to enhance AES transparency for both teachers and students by utilizing typical topic-based course structures as the foundation for designing all aspects of an AES from the domain model to the end-user interface. We illustrate the details of the topic-based personalization technology, with the help of the Web-based educational service QuizGuide—the first system to implement it. QuizGuide applies the topic-based personalization to guide students to the right learning material in the context of an undergraduate C programming course. While having a number of architectural and practical advantages, the suggested coarse-grained personalization approach deviates from the common practices toward knowledge modeling in AES. Therefore, we believe that several aspects of QuizGuide required a detailed evaluation—from modeling accuracy to the effectiveness of adaptation. The paper discusses how this new student modeling approach can be evaluated, and presents our attempts to evaluate it from multiple different prospects. The evaluation of QuizGuide across several consecutive semesters demonstrates that, although topics do not always support precise user modeling, they can provide a basis for successful personalization in AESs.", "publicationTitle": "User Modeling and User-Adapted Interaction", "publisher": "", "place": "", "date": "2015/07/10", "volume": "25", "issue": "4", "section": "", "partNumber": "", "partTitle": "", "pages": "371-424", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "User Model User-Adap Inter", "DOI": "10.1007/s11257-015-9164-4", "citationKey": "", "url": "http://link.springer.com/article/10.1007/s11257-015-9164-4", "accessDate": "2016-04-20T01:54:39Z", "PMID": "", "PMCID": "", "ISSN": "0924-1868, 1573-1391", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "link.springer.com", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Adaptive educational hypermedia", "type": 1 }, { "tag": "Adaptive 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