NCPR 2013 - Workshop: New Challenges in e-Commerce Product Recommendations

The “New Challenges in e-Commerce Product Recommendations” Workshop solicits submissions in the area of e-commerce product recommendations. Over the past decade, researchers in recommender systems have focused on algorithms such as matrix factorization and their application to relatively static and long-lived content catalogs such as movies. However, the continued surge of e-Commerce has surfaced a lot of new challenges that directly influence the design of algorithms. Our desire is to foster a discussion on this topic by bringing together industry leaders who have developed these experiences and connecting them with researchers in the field of Machine Learning as well as to broaden the areas of research in this space.
Recommending products in e-Commerce poses some unique scientific challenges that we would like to discuss in this workshop:
1. Hybrid Discovery Experiences ? Customers use a combination of search, browse and recommendations to find products. Today, these experiences are mostly siloed. Hybrid experiences such as personalized search and personalized browse offer new ways to improve the overall product discovery experience.
2. Context Awareness ? Understanding the user’s intent or their need is very important. Today, recommendations are made in many contexts such as item page, checkout page, search result page, or even within the consumption experience such as at the end of an e-book or movie.
3. Breadth of Selection ? e-Commerce spans a wide and growing range of products from movies and music to books, games, electronics, apparel, groceries, automotive, industrial supplies, pet supplies, home and garden, sporting goods etc.
4. Mobile ? The mobile setting allows novel discovery experiences on phones and tablets which are challenged by small surface area.
5. Social ? In today’s world, data from social networks are easily accessible to recommender systems. This provides additional signals to personalize product discovery as well as leverage homophily.
6. Evaluation ? In e-Commerce, root-mean-squared-error (RMSE) or mean-reciprocal rank are only proxies for evaluating customer satisfaction. Also, offline and online evaluation can vary widely.
7. Sparsity/Cold Start ? People watch movies and listen to music often ? but they don’t buy a juicer that often.
8. Age Progression ? Recommendations for kids, students and parents need to be age aware. Parents who purchased size 2 diapers a few months back cannot be recommended more size 2 diapers after a while. Same with textbooks for students.
9. Product Meta Data ? Understanding product compatibility is very important for recommendations in categories such as electronics and automotive. Unfortunately such metadata is rarely available or is noisy.
10. Periodicity ? Some products get consumed regularly (e.g., food) and the value of recommendations is also in discovering renewal patterns.
11. Free content ? With the surge of free digital content ? books, music, movies, apps and games, recommender systems need to find the right balance between free vs. paid content. The long-term value of free content is important to quantify.
Submissions
Submissions should be in UAI format (http://auai.org/uai2013/cfp.shtml - Anchor_CFP), limited to 6 pages. Papers must be submitted electronically in PDF format via email to mailto:uai2013-AT-amazon.com.
Important Dates
? May 19th: Full Paper Submission
? June 2nd: Author Notification
? July 15th: Workshop (following the UAI2013 main conference, July 12-14)
Workshop Format
? Morning
o 9:00 am ? 9:15 am: Welcome
o 9:15 am ? 10:00 am: Keynote 1
o 10:00 am ? 11:00 am: Oral Presentations 1
o 11:00 am ? 11:15 am: Coffee Break
o 11:15 am ? 12:00 am: Poster Session 1
? Lunch:
o 12:00 am ? 1:00 pm: Lunch
? Afternoon:
o 1:00 pm ? 1:45 pm: Keynote 2
o 1:45 pm ? 2:45 pm: Oral Presentations 2
o 2:45 pm ? 3:00 pm: Coffee Break
o 3:00 pm ? 4:00 pm: Poster Session 2
o 4:00 pm ? 5:00 pm: Panel Discussion
? Evening:
o 5:00 pm ? 7:00 pm: Amazon.com sponsored Social Event
Organizers
? Srikanth Thirumalai, Director of Personalization, Amazon.com
? Ralf Herbrich, Director, Machine Learning, Amazon.com