US007962505B2
`
`(12) United States Patent
`US 7,962,505 B2
`(10) Patent No.:
`Torrens et al.
`(45) Date of Patent:
`Jun. 14, 2011
`
`(54) USER TO USER RECOMMENDER
`
`(75)
`
`Inventors: Marc Torrens, Corvallis, OR (US); Pere
`Ferreras Barcelona (ES)
`
`(73) Assignee: Strands, Inc., Corvallis, OR (US)
`~
`.
`.
`.
`.
`.
`( * ) Notice.
`Subject. to any disclaimer, the term ofthis
`patent Is extended or adjusted under 35
`U.S.C. 154(b) by 0 days.
`
`5,918,014 A
`5,950,176 A
`2:823:21“: :
`6,112,186 A *
`6,134,532 A
`6,345,288 Bl
`6,346,951 B1
`6,347,313 B1
`6,381,575 B1
`6,430,539 B1
`
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`8/2000 Bergh et a1.
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`2/2002 Ma
`4/2002 Martin
`8/2002 Lazarus
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`
`..................... 705/10
`
`(21) App1.N0.: 11/641,619
`
`FOREIGN PATENT DOCUMENTS
`
`(22)
`65
`
`(
`
`)
`
`Filed:
`
`Dec. 19, 2006
`D t
`t'
`P '
`P bl'
`a a
`“or u 1“ 1°"
`US 2007/0203790 A1
`Aug. 30, 2007
`
`(51)
`
`Related US. Application Data
`(60) Provisional application No. 60/752,102, filed on Dec.
`19’ 2005'
`Int. Cl.
`(200601)
`G06F 7/00
`(2006.01)
`G06F 17/00
`707/767
`(52) US. Cl.
`.......................................
`(58) Field of Classification Search .................... 705/10;
`707/1041, 767
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`
`
`
`(56)
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`57
`
`ABSTRACT
`
`)
`(
`Disclosed are embodiments of systems and methods for rec-
`ommending relevant users to other users in a user community.
`In one implementation of such a method, two different sets of
`data are considered: a) music (or other items) that users have
`been listening to (or otherwise engaging), and b) music (or
`other items) recommendations that users have been given. In
`some embodiments, pre-computation methods allow the sys-
`.
`.
`.
`tem to efficrently compare Item sets and recommended Item
`sets among the users in the community. Such comparisons
`may also comprise metrics that the system can use to figure
`.
`.
`out Wthh users should be recommended for a given target
`usen
`
`15 Claims, 14 Drawing Sheets
`
`115
`
`Item
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`Recommender
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`12°
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`
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`

`

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`2001/0056434 A1
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`2002/0059094 A1
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`Page 00002
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`

`

`US 7,962,505 B2
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`2008/0027881 A1
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`10/2007 Walser
`10/2007 Bradley
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`* cited by examiner
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`US. Patent
`
`Jun. 14, 2011
`
`Sheet 1 of 14
`
`US 7,962,505 B2
`
`115
`
`
`
`Item
`Recommender
`110
`
`
`130
`
`118
`
`120
`
`FIG. 1
`
`Page 00006
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`US. Patent
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`Jun. 14, 2011
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`Sheet 2 of 14
`
`US 7,962,505 B2
`
`(0,0,1)
`
`Guidance
`
`(1,0,0)
`
`FIG. 2
`
`Page 00007
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`Page 00007
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`US. Patent
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`Jun. 14, 2011
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`Sheet 3 of 14
`
`US 7,962,505 B2
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`320
`
`
`
` Community
`
`
`Requests/Responses
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`Data Feed
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` 330
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`310
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`
`Page 00008
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`US. Patent
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`Jun. 14, 2011
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`Sheet 4 of 14
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`US 7,962,505 B2
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`FIG. 4
`
`Page 00009
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`Page 00009
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`US. Patent
`
`Jun. 14, 2011
`
`Sheet 5 of 14
`
`US 7,962,505 B2
`
`
`
`Recommender
`Servlet
`
`UserToUserRecommender
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`
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`
`Page 00010
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`Page 00010
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`

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`US. Patent
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`Jun. 14, 2011
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`Sheet 6 of 14
`
`US 7,962,505 B2
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`
`
`UserToUserRecommender
`
` UserToUserManager Request
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`Cache
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`620
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`Page 00011
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`Page 00011
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`

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`US. Patent
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`Jun. 14, 2011
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`Sheet 7 of 14
`
`US 7,962,505 B2
`
`UserToUserManager
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`Services
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`720
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`Page 00012
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`

`

`US. Patent
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`Jun. 14, 2011
`
`Sheet 8 of 14
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`US 7,962,505 B2
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`US. Patent
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`Jun. 14, 2011
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`Sheet 9 of 14
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`US 7,962,505 B2
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`Page 00014
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`

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`US. Patent
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`Jun. 14, 2011
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`Sheet 10 of 14
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`US 7,962,505 B2
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`Page 00015
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`US. Patent
`
`Jun. 14, 2011
`
`Sheet 11 of 14
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`US 7,962,505 B2
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`1 O4
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`US. Patent
`
`Jun. 14, 2011
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`Sheet 12 of 14
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`US 7,962,505 B2
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`US. Patent
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`Jun. 14, 2011
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`Sheet 14 of 14
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`US 7,962,505 B2
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`US 7,962,505 B2
`
`1
`USER TO USER RECOMMENDER
`
`RELATED APPLICATIONS
`
`This application claims the benefit under 35 U.S.C. §119
`(e) of US. Provisional Patent Application No. 60/752,102
`filed Dec. 19, 2005, and titled “User to User Recommender,”
`which is incorporated herein by specific reference.
`
`BRIEF DESCRIPTION OF THE DRAWINGS
`
`Understanding that drawings depict only certain preferred
`embodiments of the invention and are therefore not to be
`
`considered limiting of its scope, the preferred embodiments
`will be described and explained with additional specificity
`and detail through the use of the accompanying drawings in
`which:
`
`FIG. 1 is a diagram showing the basic components and
`sources for a user profile according to one embodiment.
`FIG. 2 depicts a graph showing the position of a target user
`“X” in ADG Space according to one embodiment.
`FIG. 3 is a diagram showing a basic architecture schema
`fora user recommender according to one embodiment.
`FIG. 4 depicts a qualitative scale indicative ofthe relevance
`of particular items to a particular user within a user commu-
`nity.
`FIG. 5 is a diagram showing a Servlet View of one embodi-
`ment of a user recommender system.
`FIG. 6 is a diagram showing a Recommender View of one
`embodiment of a user recommender system.
`FIG. 7 is a diagram showing a Manager View of one
`embodiment of a user recommender system.
`FIG. 8 is a core Unified Modeling Language (UML) dia-
`gram of one embodiment of a user recommender system.
`FIG. 9 is a diagram depicting an example of the informa-
`tion that can be extracted from a GraphPlotter tool used with
`one embodiment of a user recommender system.
`FIG. 10 is a graph representing relationships between types
`of listeners according to a “Frequency/Knowledge” model.
`FIG. 11A is a representation in matrix form of a metric
`describing the similarity values between collections of media
`items.
`
`FIG. 11B provides a weighted graph representation for the
`associations within a collection of media items. Each edge
`between two media items is annotated with a weight repre-
`senting the value of the metric for the similarity between the
`media items.
`
`10
`
`15
`
`20
`
`25
`
`30
`
`35
`
`40
`
`45
`
`FIG. 12 is a block diagram ofone method for selecting a set
`of media items corresponding to an initial set of media items
`in accordance with an embodiment of the invention.
`
`50
`
`FIG. 13 is a simplified, conceptual diagram of a knowledge
`base or database comprising a plurality of mediasets.
`
`DETAILED DESCRIPTION OF PREFERRED
`EMBODIMENTS
`
`In the following description, certain specific details ofpro-
`gramming, software modules, user selections, network trans-
`actions, database queries, database structures, etc., are pro-
`vided for a thorough understanding of the specific preferred
`embodiments of the invention. However, those skilled in the
`art will recognize that embodiments can be practiced without
`one or more of the specific details, or with other methods,
`components, materials, etc.
`In some cases, well-known structures, materials, or opera-
`tions are not shown or described in detail in order to avoid
`
`obscuring aspects of the preferred embodiments. Further-
`
`55
`
`60
`
`65
`
`2
`more, the described features, structures, or characteristics
`may be combined in any suitable manner in a variety of
`alternative embodiments. In some embodiments, the method-
`ologies and systems described herein may be carried out
`using one or more digital processors, such as the types of
`microprocessors that are commonly found in PC’s, laptops,
`PDA’ s and all manner of other desktop or portable electronic
`appliances.
`Disclosed are embodiments of systems and methods for
`recommending users to other users in a user community. As
`used herein, a “user recommender” is a module integrated in
`a community of users, the main function of which is to rec-
`ommend users to other users in that community. There may be
`a set of items in the community for the users ofthe community
`to interact with. [INSERTED PARAGRAPH BREAK]
`There may also be an item recommender to recommend
`other items to the users. Examples of recommender systems
`that may be used in connection with the embodiments set
`forth herein are described in US. patent application Ser. No.
`11/346,818 titled “Recommender System for Identifying a
`New Set of Media Items Responsive to an Input Set of Media
`Items and Knowledge Base Metrics,” and US. patent appli-
`cation Ser. No. 1 1/048,950 titled “Dynamic Identification of
`a New Set of Media Items Responsive to an Input Mediaset,”
`both of which are hereby incorporated by reference. A
`description of the former item recommender system, appli-
`cation Ser. No. 1 1/346,818 is set forth below with reference to
`drawing FIGS. 11A,11B,12 and 13.
`As used herein, the term “media data item” is intended to
`encompass any media item or representation of a media item.
`A “media item” is intended to encompass any type of media
`file which can be represented in a digital media format, such
`as a song, movie, picture, e-book, newspaper, segment of a
`TV/radio program, game, etc. Thus, it is intended that the
`term “media data item” encompass, for example, playable
`media item files (e.g., an MP3 file), as well as metadata that
`identifies a playable media file (e. g., metadata that identifies
`an MP3 file). It should therefore be apparent that in any
`embodiment providing a process, step, or system using
`“media items,” that process, step, or system may instead use
`a representation of a media item (such as metadata), and vice
`versa.
`
`The user recommender may be capable of selecting rel-
`evant users for a given target user. To do so, users should be
`comparable entities. The component that defines a user in a
`community may be referred to as the user profile. Thus, a user
`profile may be defined by defining two sets, such that com-
`paring two users will be a matter of intersecting their user
`profile sets. For example, with reference to FIG. 1, the first set
`may be the “items set,” referenced at 110 in FIG. 1, which
`may contain the most relevant items 115 for a particular user
`118. The second set may be the “recommendations set,” ref-
`erenced at 120 in FIG. 1, which may contain the most relevant
`recommended items for user 118. The items set 110 can be
`
`deduced by the item usage and/or interaction of a certain user
`with certain items, whereas the recommendations set can be
`deduced by using an item recommender 130. In some cases,
`the items set 110 canbe used as the input for the recommender
`130, thereby obtaining a recommendations set as the output.
`
`AN EXAMPLE OF AN ITEM RECOMMENDER
`
`A system identifies a new set ofrecommended media items
`in response to an input set of media items. The system
`employs a knowledge base consisting of a collection ofme