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SIGIR’13 Debrief

28 th July - 1 st August Trinity College Dublin (TCD), Dublin, Ireland. SIGIR’13 Debrief. WING Monthly Meeting (Nov 2013) Jovian Lin. Full Paper Acceptance (20%). Top 5 Countries (according to author affiliation).

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SIGIR’13 Debrief

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  1. 28th July - 1st August Trinity College Dublin (TCD), Dublin, Ireland SIGIR’13 Debrief WING Monthly Meeting (Nov 2013) Jovian Lin

  2. Full Paper Acceptance(20%)

  3. Top 5 Countries (according to author affiliation)

  4. Top 5 Technical Areas(based on primary keyword assigned by accepted papers)

  5. Short Paper Acceptance(34%)

  6. Demonstrations Acceptance(50%)

  7. Additional Stats • 7 workshops • 10 tutorials • Doctoral Consortium hosted 11 students(from 10 countries and 11 institutions)

  8. PHOTOS

  9. Trinity College Dublin (TCD) • Founded in 1592. • One of the oldest universities in Europe. • 35 acre campus located in the heart (<3) of Dublin city.

  10. Select an interesting paper in SIGIR’13 • “Opportunity model for e-commerce recommendation: right product; right time” • By Jian Wang & Yi Zhang (UC Santa Cruz)

  11. “Opportunity model for e-commerce recommendation: right product; right time” – Jian Wang & Zhang Yi • Most of existing e-commerce recommender systems aim to recommend the right product to a user; • based on whether the user is likely to purchase or like a product. • But the authors say: • “the effectiveness of recommendations also depends on the time of the recommendation.”

  12. “Opportunity model for e-commerce recommendation: right product; right time” – Jian Wang & Zhang Yi • Example: • User purchases e a replacement battery after 2 years; • and purchases a new laptop in another 2 years. • Thus it’s not a good idea to recommend a new laptop or a replacement battery right after the user purchased the new laptop.

  13. “Opportunity model for e-commerce recommendation: right product; right time” – Jian Wang & Zhang Yi • What their work does: • Model that estimates the joint probability of a user making a follow-up purchase of a particular product at a particular time. • i.e., calculate the probability of a user buying item i at time x • Why I chose this work: • They analyze the recommendation process at a deeper level – a quality that I focus on as well.

  14. THANK YOU

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