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by:Purshottam vermaby:Purshottam
Recommender Systems with deep learning
By:- Purshottam Verma
The world is an over-crowded place
They all want to get our attention
Can Google help?
• Yes, but only when we really know what
we are looking for
• What if I just want some interesting music
tracks?
– Btw, what does it mean by “interesting”?
But, what do recommender
systems do, exactly?
1. Predict how much you may like a certain
product/service
2. Compose a list of N best items for you
3. Compose a list of N best users for a certain
product/service
4. Explain to you why these items are recommended to
you
5. Adjust the prediction and recommendation based on
your feedback and other people
Ever heard of
• GroupLens?
• Amazon recommendation?
• Netflix Cinematch?
• Google News personalization?
• Netflix Prize $1mil challenge?
• Strands?
• TiVo?
• Findory?
Want some evidences?
(Celma & Lamere, ISMIR 2007)
• Netflix:
– 2/3 rented movies are from recommendation
• Google News
– 38% more click-through are due to
recommendation
• Amazon
– 35% sales are from recommendation
Content-based method
• Web page: words, hyperlinks, images, tags, comments,
titles, URL, topic
• Music: genre, rhythm, melody, harmony, lyrics, meta data,
artists, bands, press releases, expert reviews, loudness,
energy, time, spectrum, duration, frequency, pitch, key,
mode, mood, style, tempo
• User: age, sex, job, location, time, income, education,
language, family status, hobbies, general interests, Web
usage, computer usage, fan club membership, opinion,
comments, tags, mobile usage
• Context: time, location, mobility, activity, socializing,
emotion
• Top-N item list:
– Find similar users, collect what they like
– Filter out those the user has rated
– Rank the remaining items by considering
• The number of times each item is liked by those users
• The popularity of the item
• The associated ratings
• The similarity between each item in the list and what the user
has rated
• Switching the role of item to user, we may have
top-N user list
Task 2,3: Top-N recommendation

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Presentation by purshotam verma

  • 1. by:Purshottam vermaby:Purshottam Recommender Systems with deep learning By:- Purshottam Verma
  • 2. The world is an over-crowded place
  • 3. They all want to get our attention
  • 4. Can Google help? • Yes, but only when we really know what we are looking for • What if I just want some interesting music tracks? – Btw, what does it mean by “interesting”?
  • 5. But, what do recommender systems do, exactly? 1. Predict how much you may like a certain product/service 2. Compose a list of N best items for you 3. Compose a list of N best users for a certain product/service 4. Explain to you why these items are recommended to you 5. Adjust the prediction and recommendation based on your feedback and other people
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  • 19. Ever heard of • GroupLens? • Amazon recommendation? • Netflix Cinematch? • Google News personalization? • Netflix Prize $1mil challenge? • Strands? • TiVo? • Findory?
  • 20. Want some evidences? (Celma & Lamere, ISMIR 2007) • Netflix: – 2/3 rented movies are from recommendation • Google News – 38% more click-through are due to recommendation • Amazon – 35% sales are from recommendation
  • 21. Content-based method • Web page: words, hyperlinks, images, tags, comments, titles, URL, topic • Music: genre, rhythm, melody, harmony, lyrics, meta data, artists, bands, press releases, expert reviews, loudness, energy, time, spectrum, duration, frequency, pitch, key, mode, mood, style, tempo • User: age, sex, job, location, time, income, education, language, family status, hobbies, general interests, Web usage, computer usage, fan club membership, opinion, comments, tags, mobile usage • Context: time, location, mobility, activity, socializing, emotion
  • 22. • Top-N item list: – Find similar users, collect what they like – Filter out those the user has rated – Rank the remaining items by considering • The number of times each item is liked by those users • The popularity of the item • The associated ratings • The similarity between each item in the list and what the user has rated • Switching the role of item to user, we may have top-N user list Task 2,3: Top-N recommendation