Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system

Sarwar, Badrul; Konstan, Joseph A.; Borchers, Al; Herlocker, Jonathan L.; Miller, Brad; Riedl, John

doi:10.1145/289444.289509

Cited by 270 publications

(142 citation statements)

References 14 publications

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“…The proposed system has been tested using the MovieLens dataset [5], and preliminary recommendation experiments using only genre information yield results that are as good as, or better than, those previously published in [1,2]. Results of experiments that utilize richer content and user preference description metadata will be reported during the conference.…”

Section: Overviewmentioning

confidence: 99%

Content-based filtering and personalization using structured metadata

Ferman¹,

Errico²,

Beek³

et al. 2002

Proceedings of the 2nd ACM/IEEE-CS Joint Conference on Digital Libraries

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Structured descriptions of multimedia content and automatically generated user profiles are used to filter content. Categories OVERVIEWUser profiles that contain compact descriptions of users' interests and personal preferences provide a method for selecting from an increasing amount of multimedia content and for reducing information overload [1,2]. A user profile can be used to (a) filter input content, so that programs or items that the user has shown interest in are presented to the user, and/or (b) request from a content distribution service the programs of interest. Emerging standards such as MPEG-7 and TV-Anytime [3,4] provide an XMLbased, interoperable framework for structured and rich description of multimedia content, along with tools that enable improved user interaction with such content, e.g., tools for representing users' preferences and history of content usage. In this paper, we propose novel algorithms for (i) automatically determining a user's profile from his/her usage history (profiling agent), and (ii) automatically filtering content according to the user's profile (filtering agent). The proposed algorithms are designed to support an MPEG-7 or TVAnytime compliant description framework, although they can also be utilized in a non-standard environment.We present a framework for integrated personalization and management of multimedia content (see Figure 1) which allows non-intrusive observation of user actions over time, enabling collection of consumption-related data without explicit user input. This usage history is subsequently utilized in conjunction with structured metadata that is available for each of the consumed content entities to identify what types of content the user prefers most. Specifically, various features are extracted from the content metadata and usage history database, and are then utilized by the proposed profiling agent that incorporates a fuzzy reasoning system to identify and update the user's preferences. The resulting preference descriptions may include a variety of descriptive attributes, such as genres, actors, channel information, etc., each of which can be assigned a weight reflecting its desirability by the user. Furthermore, the preference descriptions support multilevel, hierarchical grouping of different attributes, and thus provide a highly flexible way of supporting a variety of applications. These descriptions are subsequently compared against the content metadata by the proposed filtering agent, to determine which content entities best match the user's interests. Each individual component of a user preference description corresponds to a test or matching operation against components of descriptions of multimedia content.After individual preference components are tested, the results of these tests are combined into a single composite test result that reflects the user's overall preferences. The filtering agent combines the individual test results according to the hierarchy of the user preferences description, where each parent test is a combination...

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Section: Overviewmentioning

confidence: 99%

Content-based filtering and personalization using structured metadata

Ferman¹,

Errico²,

Beek³

et al. 2002

Proceedings of the 2nd ACM/IEEE-CS Joint Conference on Digital Libraries

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“…It has several limitations however. 9,10 First, it often provides bad recommendations since it only considers the pre-specified contents for products/services. If two items have the multiattribute approaches (eg preference regression) to explain consumer's preference for products by a set of their attributes.…”

Section: Introductionmentioning

confidence: 99%

A new recommender system to combine content-based and collaborative filtering systems

Kim

2001

J Database Mark Cust Strategy Manag

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“…A particular Groupspace may contain built-in tools and services to be used by participants, e.g. to add arbitrary annotations to particular artifacts, hold real-time chat sessions, add hypertext links on top of (and separate from) artifacts in the system, semi-automatically propagate knowledge among participants (in the manner of a recommender system [13]), etc. We use the term Groupviews to describe multiuser, scalable user interfaces used to navigate and work in a Groupspace.…”

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confidence: 99%

CHIME: A Metadata-Based Distributed Software Development Environment

Dossick¹,

Kaiser²

1999

Software Engineering — ESEC/FSE ’99

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Abstract. We introduce CHIME, the Columbia Hypermedia IMmersion Environment, a metadata-based information environment, and describe its potential applications for internet and intranet-based distributed software development. CHIME derives many of its concepts from Multi-User Domains (MUDs), placing users in a semi-automatically generated 3D virtual world representing the software system. Users interact with project artifacts by "walking around" the virtual world, where they potentially encounter and collaborate with other users' avatars. CHIME aims to support large software development projects, in which team members are often geographically and temporally dispersed, through novel use of virtual environment technology. We describe the mechanisms through which CHIME worlds are populated with project artifacts, as well as our initial experiments with CHIME and our future goals for the system.

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Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system

Cited by 270 publications

References 14 publications

Content-based filtering and personalization using structured metadata

Content-based filtering and personalization using structured metadata

A new recommender system to combine content-based and collaborative filtering systems

CHIME: A Metadata-Based Distributed Software Development Environment

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