Recommender Systems and Matrix Factorization
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Questions and Answers

What motivates the use of a logarithmic term when computing TF and IDF values?

  • The concept of conditional independence of terms and documents.
  • The TF monotonicity assumption.
  • The user background describing general information about the user.
  • Zipf’s law about the frequency of word occurrences in English texts. (correct)

    • What is the assumption about terms that appear infrequently in a document according to the TF monotonicity assumption?

  • They are less important than terms that appear in many documents.
  • They are more important than terms that appear in many documents. (correct)
  • They have no effect on the document's relevance.
  • They are only relevant to the user's background.

    • What is the relationship between LSA and LDA?

  • LDA is a probabilistic variant of LSA. (correct)
  • LSA is a topic modeling approach, while LDA is a metric.
  • They are unrelated topic modeling approaches.
  • LSA is a probabilistic variant of LDA.

    • What is a characteristic of the topic modeling approaches pLSA and LDA?

    <p>They assume conditional independence of terms and documents, given a topic z.</p> Signup and view all the answers

    What type of information does the user background describe?

    <p>General, rather static information about the user, e.g., knowledge or demographics.</p> Signup and view all the answers

    What does the average precision (AP) metric account for?

    <p>Relevant items not in the recommendation list, therefore, implicitly factoring in the rank of the relevant items.</p> Signup and view all the answers

    What is the purpose of context acquisition?

    <p>To acquire information about the user, either explicitly, implicitly, or by inferring.</p> Signup and view all the answers

    What describes the purpose the content creator had in mind when creating an item?

    <p>User intent.</p> Signup and view all the answers

    What is one of the reasons why people use recommender systems according to Herlocker et al.?

    <p>To find all good items</p> Signup and view all the answers

    What is the purpose of the regularization term in the optimization function of an SGD-trained MF model?

    <p>To avoid unbound values in the w and h vectors</p> Signup and view all the answers

    What is a common choice for regularization when using stochastic gradient descent (SGD) to create a MF model?

    <p>Tikhonov regularization</p> Signup and view all the answers

    What is the effect of the mutual proximity approach on hubness in recommender systems?

    <p>It decreases hubness</p> Signup and view all the answers

    Is a user bias factor required when computing memory-based CF with binary ratings?

    <p>No, it is not required</p> Signup and view all the answers

    How does user-based CF scale with the number of users and items?

    <p>It does not scale well with the number of users and items</p> Signup and view all the answers

    What is the main idea behind the IDF monotonicity assumption?

    <p>Terms that appear in only a few documents are more important</p> Signup and view all the answers

    Why is content-based filtering well-suited to recommend “long tail” items?

    <p>Because it is based on content features</p> Signup and view all the answers

    Study Notes

    Recommender Systems

    • Reasons for using recommender systems include finding all good items, finding good items in context, and helping others.
    • The regularization term in the optimization function of an SGD-trained MF model is used to avoid unbound values in the w and h vectors and to prevent overfitting.

    Model-Based Collaborative Filtering

    • A common choice for regularization is Tikhonov regularization.
    • The mutual proximity approach can effectively reduce hubness in recommender systems.

    Memory-Based Collaborative Filtering

    • In case of binary ratings, no user bias factor is required when computing memory-based CF.
    • User-based CF (in the memory-based variant) tends to scale poorly with the number of users and items.

    Information Retrieval

    • The IDF monotonicity assumption states that terms that appear in only a few documents of the corpus are more important than terms that appear in many documents.
    • The use of a logarithmic term when computing TF and IDF values is motivated by Zipf’s law about the frequency of word occurrences in English texts.
    • The TF monotonicity assumption states that terms that appear frequently in a document are more important than terms that appear infrequently.

    Content-Based Filtering

    • Content-based filtering is well-suited to recommend “long tail” items because content features are less affected by popularity biases than user ratings.

    Topic Modeling

    • The topic modeling approaches pLSA and LDA assume conditional independence of terms and documents, given a topic z.
    • LSA is not a probabilistic variant of LDA.

    Context Awareness

    • Context acquisition can be explicit, implicit, or inferring.
    • The user background describes general, rather static information about the user, e.g., knowledge or demographics.
    • The user intent is not the purpose the content creator had in mind when creating the item.

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    Quiz on recommender systems, including reasons for using them and matrix factorization techniques with stochastic gradient descent.

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