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RECOMMENDER SYSTEMS

Sean Blong Presents:

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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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Who uses them…?

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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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3 Types of Recommendation

Personalized recommendation Social recommendation Item recommendation

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Personalized Recommendation

Recommend items based on the individual's past behavior.

Examples:PandoraNetflixGoogle

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Netflix (Personalized Recommendation)

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Social Recommendations

Recommend items based on the past behavior of similar users

Examples:Facebook friend recommendationsAmazonNetflix

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Amazon (Social Recommendation)

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Item Recommendation

Recommend things based on the item itself

Examples:AmazonMost clothing companiesPandora

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Urban Outfitters (Item Recommendation)

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So how do they work…?

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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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Item/Social Recommendation

Personal/Item Recommendation

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How it applies to Advanced Clothing Solutions…

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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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A Look at the Database…

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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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Problems with Social Recommendation vs. Personal Recommendation

Social: User:

Social: User:

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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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Nearest Neighbor

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Latent Factor

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Matrix Factorization (cont.) Other items to consider:

Adding biasesAdditional input sourcesTemporal dynamicsInputs with varying confidence levels

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Questions?