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Recommender systems

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发布2021-05-19 13:33:14
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发布2021-05-19 13:33:14
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文章被收录于专栏:hsdoifh biuwedsyhsdoifh biuwedsy

Lecture 5: Recommender systems

-understand what is a recommender system

  • A recommender system is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to an item
  • A Recommender System refers to a system that is capable of predicting the future preference of a set of items for a user, and recommend the top items.

-understand why missing data is an important issue for recommender systems

-understand what is collaborative filtering and the difference between user based and item based collaborative filtering

  • Collaborative Filtering: Make predictions about a user’s missing data according to the behaviour of many other users
    • Look at users collective behavior
    • Look at the active user history
    • Combine!
  • Approach
    • User based methods
      • Identify like-minded users
    • Item based methods
      • Identify similar items
    • Model (matrix) based methods (not examinable)
      • Simultaneously identify like-minded users and items
  • User based
    • Achieve good quality in practice
    • The more processing we push offline, the better the method scale
    • However:
      • User preference is dynamic
        • High update frequency of offline-calculated information
      • No recommendation for new users
        • We don’t know much about them yet
  • Item based
    • Search for similarities among items
    • All computations can be done offline
    • Item-Item similarity is more stable than user-user similarity
      • No need for frequent updates:
    • Same as in user-user similarity but on item vectors
      • Find similar items to the one whose rating is missing
      • E.g. For item ii compute its similarity to each other item ij
    • Offline phase. For each item
      • Determine its k-most similar items
      • Can use same type of similarity as for user-based
    • Online phase:
      • Predict rating raj for a given user-item pair as a weighted sum over the k-most similar items that they rated

-understand the difference between i) user based methods for collaborative filtering and ii)item based methods for collaborative filtering

  • Same as in user-user similarity but on item vectors
    • Find similar items to the one whose rating is missing
    • E.g. For item ii compute its similarity to each other item ij

-understand how to measure user-user similarity via transformation of the Euclidean distance

  • Identify like-minded users
  • Measure similarity
    • Method 1:
      • Compute mean value for User1’s missing values
      • Compute mean value for User2’s missing values
      • Compute squared Euclidean distance between resulting vectors
  • Compute mean (average) value for User1’s missing values (18.1)Compute mean value for User2’s missing values (14.1)
  • Compute Euclidean distance between resulting rows
  • Convert the distance into a similarity (high similarity for low distance, low similarity for high distance)
  • Method 2:
    • Compute squared Euclidean distance between vectors, summing only pairs without missing values
    • Scale the result, according to percentage of pairs with a missing value
  • Other:
    • Correlation
    • Cosine similarity

-when performing user-user similarity, understand how to select neighbors and make a prediction of the missing item

  • Select neighbours & make prediction
    • At runtime
      • Need to select users to compare to
      • Could choose the top-k most similar users
      • Combining: prediction of rating is the average of the values from the top-k similar users
    • Can make more efficient by computing clusters of users offline
      • At runtime find nearest cluster & use the centre of the cluster as the rating prediction
      • Faster but less accurate

-understand how to predict missing ratings for an item using item-item similarity.

  • Search for similarities among items
  • All computations can be done offline
  • Item-Item similarity is more stable than user-user similarity
    • No need for frequent updates:
  • Find similar items to the one whose rating is missing
  • Same as in user-user similarity but on item vectors
    • Find similar items to the one whose rating is missing
    • E.g. For item ii compute its similarity to each other item ij
  • Offline phase. For each item
    • Determine its k-most similar items
    • Can use same type of similarity as for user-based
  • Online phase:
    • Predict rating raj for a given user-item pair as a weighted sum over the k-most similar items that they rated

-appreciate the difference between the online and offline phases for item based collaborative filtering

  • Offline phase. For each item
    • Determine its k-most similar items
    • Can use same type of similarity as for user-based
  • Online phase:
    • Predict rating raj for a given user-item pair as a weighted sum over the k-most similar items that they rated

-the material on matrix factorisation does not need to be known

原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。

如有侵权,请联系 cloudcommunity@tencent.com 删除。

原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。

如有侵权,请联系 cloudcommunity@tencent.com 删除。

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