Semantic Network-driven
News Recommender
Systems
A Celebrity Gossip Use Case
Marco Fossati
claudio giuliano
Giovanni Tumma...
Post-click news
recommendation
2
Typical approaches
ª  Issues
²  Data sparsity
²  Implicit user profile
interpretation
²  Lack of
recommendation
explan...
Challenge
Provide interesting recommendations
to an anonymous user
via large scale structured knowledge bases
4
Proposed approach
Entity
linking
Lindsay
Lohan
Dina
Lohan
Michael
Lohan
Entity listSource article
Extract types
+ properti...
A semantic recommender
SELECT articles
that mention
entities in
relation R with X!
Candidate
Articles
Entity-linked
Corpus...
Evaluation
ª Online with real users
ª Crowdsourcing
7
“Which is the recommendation
that best attracts your attention?”
Objectives
ª Recommendation strategies competition
² Ours (hybrid)
² Baseline (LSA+BOW)
² Fake (random)
ª Specific ex...
Job unit example
9
Results
♣ indicates statistical significance difference between the
baseline and our method, with p<0.001
10
Objective Fak...
Discussion
ª Significant difference with specific
explanations
² Disappears while decreasing the
specific explanation co...
Future work
ª Methodologies for
² Generic semantic recommenders
building
² Natural language specific
explanations
ª Mo...
Conclusion
ª Our approach enables
² Rich explanations
² Diverse/unusual recommendations
ª Evaluation shows
² No expla...
Thanks for your attention
Marco Fossati
fossati@fbk.eu
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Semantic Network-driven News Recommender Systems

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Talk at SeRSy workshop, co-located at ISWC 2012, Boston, U.S.

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  • Here is the scenario we are located in: a GENERALLY anonymous user has clicked on a news link and directly lands at the the article page. A recommendation system suggests other related articles.
  • Let me now spend a few words on the most common approaches for news recommendation. Although it is known they work effectively, they are affected by some limitations.
  • We want to investigate if it is possible to
  • Let’s have a closer look at what is a semantic recommender. Basically it is composed of a SPARQL query AND an explanation template. which is ran against a triple store. The triple store contains both the Freebase graph and a pre-collected corpus of articles that are linked with entities, in the same way we linked the source article. The query takes as input a source article entity, performs a join between the 2 graphs and directly outputs the set of candidate articles.
  • …we want to ask users which is the recommendation that best attract their attention given different strategies
  • Semantic Network-driven News Recommender Systems

    1. 1. Semantic Network-driven News Recommender Systems A Celebrity Gossip Use Case Marco Fossati claudio giuliano Giovanni Tummarello Web of Data Unit Fondazione Bruno Kessler Trento, Italy fossati@fbk.eu
    2. 2. Post-click news recommendation 2
    3. 3. Typical approaches ª  Issues ²  Data sparsity ²  Implicit user profile interpretation ²  Lack of recommendation explanation 3 ª Collaborative Filtering ² User profile ª Content-based ² Keyword-driven ª Issues ² Lack of recommendation explanation ² “More of the same”
    4. 4. Challenge Provide interesting recommendations to an anonymous user via large scale structured knowledge bases 4
    5. 5. Proposed approach Entity linking Lindsay Lohan Dina Lohan Michael Lohan Entity listSource article Extract types + properties Fully described entity set Pre-built recommenders Lindsay Lohan: [actress, dated, American, legal problems] … dated dead celebrities legal problems … 5 Candidate object entities W. Walderrama Leo Di Caprio Britney Spears Johnny Depp Candidate recommended articles •  Article A •  Article B •  Article C •  Article X •  Article Y •  Article Z 1.  Article B 2.  Article Z 3.  Article X Ranking and winner Triggerable recommenders Legal problems Dated
    6. 6. A semantic recommender SELECT articles that mention entities in relation R with X! Candidate Articles Entity-linked Corpus 1 2 Triple Store Source article entity (X) •  Article 1 •  Article 3 •  … •  Article 2 •  Article 4 •  … <X, R, Y> <X, R, Z> 6 Specific Explanations Lindsay dated Lindsay dated Y Z Explanation template “X dated Y. See what Y did”! SELECT articles that mention Y who dated X! (X)
    7. 7. Evaluation ª Online with real users ª Crowdsourcing 7 “Which is the recommendation that best attracts your attention?”
    8. 8. Objectives ª Recommendation strategies competition ² Ours (hybrid) ² Baseline (LSA+BOW) ² Fake (random) ª Specific explanation ª Simplified specific explanation ª No specific explanation 8 Lindsay dated Leo.! Read more about him! Read more about! who dated Lindsay! Related stories! selected for you!
    9. 9. Job unit example 9
    10. 10. Results ♣ indicates statistical significance difference between the baseline and our method, with p<0.001 10 Objective Fake % Baseline % Ours % Specific explanation 3.33 23.33 73.33♣ Simplified specific explanation 5.88 41.17 52.94 Without specific explanation 13.63 37.5 48.86 ª 810 judgments ª 36.6 $ ª 10 jobs ª 10 units/job
    11. 11. Discussion ª Significant difference with specific explanations ² Disappears while decreasing the specific explanation complexity ª Results seem comparable with the baseline even in absence of explanation 11
    12. 12. Future work ª Methodologies for ² Generic semantic recommenders building ² Natural language specific explanations ª More experiments on the recommendation quality 12
    13. 13. Conclusion ª Our approach enables ² Rich explanations ² Diverse/unusual recommendations ª Evaluation shows ² No explanation à comparable results ² With explanation à significantly better 13
    14. 14. Thanks for your attention Marco Fossati fossati@fbk.eu

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