Guangyuan Piao, John G. Breslin

Unit for Social Semantics

20th International Conference on Knowledge Engineering and Knowledge Management Bologna, Italy, 19-23, November, 2016

Interest Representation, Enrichment, Dynamics, and Propagation: A Study of the Synergetic Effect of Different User Modeling Dimensions for Personalized Recommendations on Twitter

2

1/3 users seek medical information and over 50% users consume news

on Social Networks

Facebook and Twitter together generate more than 5 billion microblogs / day

[SOURCE] Semantic Filtering for Social Data, Amit et al., Internet Computing’16

Background – User Modeling

content enrichment

analysis & user modeling

interest profile

?

personalized content recommendations

(How) can we infer user interest profiles

that support the content recommender?

3 [SOURCE] Analyzing User Modeling on Twitter for Personalized News Recommendations, UMAP’11

4

Background – User Modeling

Dimensions

representation enrichment

propagation dynamics

5

Dimensions

representation

Bag of Words

Topic Modeling

Bag of Concepts

Mixed Approach

Background – User Modeling

Bag-of-Concepts example

dbpedia:The_Black_Keys (3)

dbpedia:Eagles_of_Death_Metal (5)

Background – User Modeling

dbpedia:The_Wombats (2)

Interest Frequency (IF)

7

Background – User Modeling

Dimensions

enrichment

8

Background – User Modeling

Dimensions

dynamics

Assumption: user interests might change over time

Background – User Modeling

Dimensions

propagation

dbpedia:The_Wombats

dbpedia:Indie_rock genre

dbpedia:The_Black_Keys

dbc:Rock_music_duos

subject

10

Background – User Modeling

Dimensions

representation enrichment

propagation dynamics

dimensions have been studied separately

11

Aim of Work

representation enrichment

propagation dynamics

Dimensions

to investigate (how) can we combine different dimensions for user modeling

12

User Modeling Framework

user interest profiles

entity extraction

primitive interests IF weighting

temporal dynamics interest propagation

primitive & propagated

interests

synset extraction

optional enabled

enrichment

IDF weighting normalization

13

Representation

•  concept-based

!  DBpedia concepts are extracted using Aylien API •  mixed approach (WordNet synset & concept-based)

!  synsets are extracted using Degemmis’s method [UMUAI]

Enrichment

•  exploring embedded URL in tweets

!  concepts or synsets are extracted from the content of URL

Interest Representation & Enrichment

14

Propagation strategy using DBpedia

•  category-based

SP: sub-pages of the category SC: sub-categories of the category

•  property-based

P: property count in DBpedia graph

Interest Propagation

15

Temporal Dynamics of User Interests

Interest decay functions

•  Long-term(Orlandi) [SEMANTiCS]

•  Long-term(Ahmed) [SIGKDD]

Long-term(Ahmedα): µ2week, µ2month, µall •  Long-term(Abel) [WebSci]

µweek = µ = e -1

µmonth = µ 2

µall = µ 3

16

Design Space of User Modeling

The design space of user modeling, spanning 2x2x2x2=16 possible user modeling strategies.

Notation

•  um( representation; enrichment; dynamics; semantics ) •  use “none” to denote a certain dimension is disabled

!  um( synset & concept; enrichment; none; none)

Dataset •  322 users: shared at least one link in the last two weeks

•  247,676 tweets in total

Experiment •  task: recommending 10 links (URLs)

•  recommendation algorithm: cosine similarity(P(u), P(i)) P(i): item (link) profile using the same modeling strategy for P(u)

•  ground truth links: links shared in the last two weeks

•  candidate links: 15,440 links

17

Experiment Setup

used for user modeling

ground truth links (URLs)

recommendation time

Results with enrichment > without enrichment

Results

synset & concept > concept

Conclusions & Future Work

•  propagation helps when using concept-based representation without enrichment

•  the most important dimensions : Content Enrichment & Interest Representation

•  investigation of how different percentages of links affect the performance

•  the best-performing strategy : um (synset & concept; enrichment; dynamics; none )

21

Thank you for your attention!

Guangyuan Piao homepage: http://parklize.github.io e-mail: guangyuan.piao@insight-centre.org twitter: https://twitter.com/parklize slideshare: http://www.slideshare.net/parklize