16 Questions
What motivates the use of a logarithmic term when computing TF and IDF values?
Zipf’s law about the frequency of word occurrences in English texts.
What is the assumption about terms that appear infrequently in a document according to the TF monotonicity assumption?
They are more important than terms that appear in many documents.
What is the relationship between LSA and LDA?
LDA is a probabilistic variant of LSA.
What is a characteristic of the topic modeling approaches pLSA and LDA?
They assume conditional independence of terms and documents, given a topic z.
What type of information does the user background describe?
General, rather static information about the user, e.g., knowledge or demographics.
What does the average precision (AP) metric account for?
Relevant items not in the recommendation list, therefore, implicitly factoring in the rank of the relevant items.
What is the purpose of context acquisition?
To acquire information about the user, either explicitly, implicitly, or by inferring.
What describes the purpose the content creator had in mind when creating an item?
User intent.
What is one of the reasons why people use recommender systems according to Herlocker et al.?
To find all good items
What is the purpose of the regularization term in the optimization function of an SGD-trained MF model?
To avoid unbound values in the w and h vectors
What is a common choice for regularization when using stochastic gradient descent (SGD) to create a MF model?
Tikhonov regularization
What is the effect of the mutual proximity approach on hubness in recommender systems?
It decreases hubness
Is a user bias factor required when computing memory-based CF with binary ratings?
No, it is not required
How does user-based CF scale with the number of users and items?
It does not scale well with the number of users and items
What is the main idea behind the IDF monotonicity assumption?
Terms that appear in only a few documents are more important
Why is content-based filtering well-suited to recommend “long tail” items?
Because it is based on content features
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.
Quiz on recommender systems, including reasons for using them and matrix factorization techniques with stochastic gradient descent.
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