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Maxwell", "lastName": "Harper" }, { "creatorType": "author", "firstName": "Joseph A.", "lastName": "Konstan" } ], "abstractNote": "The MovieLens datasets are widely used in education, research, and industry. They are downloaded hundreds of thousands of times each year, reflecting their use in popular press programming books, traditional and online courses, and software. These datasets are a product of member activity in the MovieLens movie recommendation system, an active research platform that has hosted many experiments since its launch in 1997. This article documents the history of MovieLens and the MovieLens datasets. We include a discussion of lessons learned from running a long-standing, live research platform from the perspective of a research organization. We document best practices and limitations of using the MovieLens datasets in new research.", "publicationTitle": "ACM Trans. Interact. Intell. 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Even though existing work has suggested a wide range of criteria, the consistency and validity of the combined criteria have not been tested. In this paper, we describe a unifying evaluation framework, called ResQue (Recommender systems' Quality of user experience), which aimed at measuring the qualities of the recommended items, the system's usability, usefulness, interface and interaction qualities, users' satisfaction with the systems, and the influence of these qualities on users' behavioral intentions, including their intention to purchase the products recommended to them and return to the system. We also show the results of applying psychometric methods to validate the combined criteria using data collected from a large user survey. The outcomes of the validation are able to 1) support the consistency, validity and reliability of the selected criteria; and 2) explain the quality of user experience and the key determinants motivating users to adopt the recommender technology. 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", "contentType": "", "charset": "", "tags": [], "relations": {}, "dateAdded": "2026-02-17T19:51:58Z", "dateModified": "2026-02-17T19:51:58Z" } }, { "key": "7M8GPQ5H", "version": 6518, "library": { "type": "group", "id": 14159, "name": "GroupLens", "links": { "alternate": { "href": "https://www.zotero.org/groups/grouplens", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/14159/items/7M8GPQ5H", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/grouplens/items/7M8GPQ5H", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1800868, "username": "maxharp3r", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/maxharp3r", "type": "text/html" } } }, "lastModifiedByUser": { "id": 7587983, "username": "Mindyzhaominzhu", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/mindyzhaominzhu", "type": "text/html" } } }, "creatorSummary": "Vig et al.", "parsedDate": "2012-09", "numChildren": 2 }, "data": { "key": "7M8GPQ5H", "version": 6518, "itemType": "journalArticle", "title": "The Tag Genome: Encoding Community Knowledge to Support Novel Interaction", "creators": [ { "creatorType": "author", "firstName": "Jesse", "lastName": "Vig" }, { "creatorType": "author", "firstName": "Shilad", "lastName": "Sen" }, { "creatorType": "author", "firstName": "John", "lastName": "Riedl" } ], "abstractNote": "This article introduces the tag genome, a data structure that extends the traditional tagging model to provide enhanced forms of user interaction. Just as a biological genome encodes an organism based on a sequence of genes, the tag genome encodes an item in an information space based on its relationship to a common set of tags. We present a machine learning approach for computing the tag genome, and we evaluate several learning models on a ground truth dataset provided by users. We describe an application of the tag genome called Movie Tuner which enables users to navigate from one item to nearby items along dimensions represented by tags. We present the results of a 7-week field trial of 2,531 users of Movie Tuner and a survey evaluating users’ subjective experience. Finally, we outline the broader space of applications of the tag genome.", "publicationTitle": "ACM Trans. Interact. Intell. Syst.", "publisher": "", "place": "", "date": "September 2012", "volume": "2", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "13:1–13:44", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1145/2362394.2362395", "citationKey": "vigTagGenomeEncoding2012a", "url": "http://doi.acm.org/10.1145/2362394.2362395", "accessDate": "2016-09-19T15:06:50Z", "PMID": "", "PMCID": "", "ISSN": "2160-6455", "archive": "", "archiveLocation": "", "shortTitle": "The Tag Genome", "language": "", "libraryCatalog": "ACM Digital Library", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Information retrieval", "type": 1 }, { "tag": "Tagging", "type": 1 }, { "tag": "conversational recommenders", "type": 1 }, { "tag": "data mining", "type": 1 }, { "tag": "machine learning", "type": 1 }, { "tag": "notion" }, { "tag": "recommender systems", "type": 1 } ], "collections": [ "7JCGB8T8", "7PRUUTU5", "VVGZQU4N" ], "relations": {}, "dateAdded": "2016-09-19T15:06:50Z", "dateModified": "2026-02-17T19:51:58Z" } }, { "key": "4V3GKEX9", "version": 6506, "library": { "type": "group", "id": 14159, "name": "GroupLens", "links": { "alternate": { "href": "https://www.zotero.org/groups/grouplens", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/14159/items/4V3GKEX9", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/grouplens/items/4V3GKEX9", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1800868, "username": "maxharp3r", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/maxharp3r", "type": "text/html" } } }, "lastModifiedByUser": { "id": 7587983, "username": "Mindyzhaominzhu", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/mindyzhaominzhu", "type": "text/html" } } }, "creatorSummary": "McNee et al.", "parsedDate": "2006", "numChildren": 2 }, "data": { "key": "4V3GKEX9", "version": 6506, "itemType": "conferencePaper", "title": "Making Recommendations Better: An Analytic Model for Human-recommender Interaction", "creators": [ { "creatorType": "author", "firstName": "Sean M.", "lastName": "McNee" }, { "creatorType": "author", "firstName": "John", "lastName": "Riedl" }, { "creatorType": "author", "firstName": "Joseph A.", "lastName": "Konstan" } ], "abstractNote": "Recommender systems do not always generate good recommendations for users. In order to improve recommender quality, we argue that recommenders need a deeper understanding of users and their information seeking tasks. Human-Recommender Interaction (HRI) provides a framework and a methodology for understanding users, their tasks, and recommender algorithms using a common language. Further, by using an analytic process model, HRI becomes not only descriptive, but also constructive. 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