social media recommendation based on people and tags
DESCRIPTION
Social Media Recommendation based on People and Tags. Ido Guy, Naama Zwerdling, Inbal Ronen, David Carmel, Erel Uziel SIGIR ’ 10. Fırat Onur Alsaran. Outline. Introduction Recommender system Recomm e nder Widget Social Media Platform Relationship Aggregation User Profile - PowerPoint PPT PresentationTRANSCRIPT
Ido Guy, Naama Zwerdling, Inbal Ronen, David Carmel, Erel Uziel
SIGIR’10
OutlineIntroductionRecommender system
Recommender WidgetSocial Media PlatformRelationship AggregationUser ProfileRecommendation Algorithm
ExperimentsConclusion
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Introduction
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IntroductionUsers are flooded with contentHow to judge the validity of so much content?As social media grows larger everyday, these
web sites are increasingly challenged to attract new users and retain existing ones.
Contribution: Study personalized recommendation of social media items
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Recommender systemRecommender Widget
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Recommender SystemLotus Connections:
A social software application suiteprofiles, activities, bookmarks, blogs,
communities, files, and wikis.Recommendation platform for the system
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Recommender systemRelationship Aggregation
SaND Models relationships through data collected across
all LC applications. Aggregates any kind of relationships between
people, items, and tags. For each user, weighted lists of PEOPLE, ITEMS
and TAGS are extracted
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Recommender systemRelationship Aggregation
SaND builds an entity-entity relationship matrix
direct relations indirect relations
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Recommender systemUser Profile
P(u): an input to the recommender engine once the user u logs into the system.
N(u): 30 related peopleT(u): 30 related tags
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Recommender systemUser Profile
Person-person relations Aggregate direct and indirect people-people
relations into a single person-person relationship. Each direct relation adds a sore of 1. Each indirect relation adds a score in the range of
(0,1].
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Recommender systemUser Profile
User-tag relations used tags
direct relation based on tags the user has used incoming tags
direct relation based on tags applied on the user indirect tags
indirect relation based on tags applied on items related on the user
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Recommender systemTag Profile Survey – participants are asked to
evaluate tags as indicators of topic of interest
Combination of used and incoming tags is the best indicator to generate T(U) from SaND system
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Recommender systemRecommendation Algorithm
d(i): number of days since the creation date of iw(u,v) and w(u,t): relationship strengths of u to
user v and tag tw(v,i) and w(t,i): relationship strengths
between v and t, respectively, to item i
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Recommender systemRecommendation Algorithm
User-item relation: authorship (0.6), membership (0.4), commenting (0.3), and tagging (0.3)
Tag-item relation: number of users who applied the tag on the item, normalized by the overall popularity of the tag.
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Evaluation5 recommenders
PBR: β=1TBR: β=0or-PTBR: β=0.5and-PTBR: β=0.5POPBR: popular item recommendation.
Each participant is assigned to one recommender
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EvaluationRecommended Items Survey
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EvaluationRecommended Items Survey
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ConclusionThe combination of directly used tags and
incoming tags produces an effective tag-based user profile.
Using tags for social media recommendation can be highly beneficial.
Combining tag and person based recommendations perform better.
Future Work:Large scale evaluationComputationally intensive algorithm may be used.
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