sean blong presents: 1. what are they…? “[…] specific type of information filtering (if)...
Post on 17-Dec-2015
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What are they…?
“[…] specific type of information filtering (IF) technique that attempts to present information items (movies, music, books, news, images, web pages, etc.) that are likely of interest to the user.”
More simply, enhances companies profits as well as the user’s shopping experience. (win-win)
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So why should you care?
Netflix PrizeOpen competition for the best collaborative
filtering algorithm to predict user ratings for films, based on previous ratings
Prize: $1,000,000Biggest competitive advantage
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Personalized Recommendation
Recommend items based on the individual's past behavior.
Examples:PandoraNetflixGoogle
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Social Recommendations
Recommend items based on the past behavior of similar users
Examples:Facebook friend recommendationsAmazonNetflix
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Item Recommendation
Recommend things based on the item itself
Examples:AmazonMost clothing companiesPandora
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Google customizes your search results based on your location and/or recent search activity.
When signed in to a Google Account, you will see even more relevant results based on your web history.
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Google's search algorithm PageRank is dependent on social recommendations (who links to a webpage)
Google also does item recommendations with its "Did you mean" feature.Try typing recursion in the search bar.
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Pandora relies on a Music Genome that consists of 400 musical attributes covering the qualities of melody, harmony, rhythm, form, composition and lyrics.
Item based recommendations based on these musical attributes.
Not a social recommendation system!!!
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Combines all 3 techniques:All recommendations are based on individual
behavior, the item itself, and the behavior of other people on Amazon.
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Goals:
Store user data:What they’ve bought/own, what they’ve tried
on, what they like/don’t like. Make recommendations:
Utilizing the Item, Social, and Personal Recommendation systems.
Utilize data to create personalized sales, deals, and coupons.i.e. Increase profits and shopping
experience!
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Challenges:
THE ALGORTHIMHow to assign similarity through tags?
○ How to assign tags? (see ER diagram)How to assign individual weights of the three
recommendation facets (personal, social, item).
How to accurately portray user’s tastes using a binary ranking system (think Pandora)
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More simply…
User: username, userid, name, address, phone
Article: articleid, type, gender, color, size, description, company name
Likes: userid, articleid, ratingSo what’s the issue…?
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The technical side of Recommendation Systems…
Latent Factor (matrix factorization) vs. Nearest NeighborLatent Factor: become popular in recent
years by combining good scalability with predictive accuracy. In addition, they offer much flexibility for modeling various real-life situations.
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Matrix Factorization (cont.) Other items to consider:
Adding biasesAdditional input sourcesTemporal dynamicsInputs with varying confidence levels
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