Introduction To Recommender Systems at Universität Bern

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How does TF-IDF apply to CBF?

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What are advantages and disadvantages of content-based recommendation techniques?

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What is the difference between predictions and recommendations?

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Exemplary flashcards for Introduction To Recommender Systems at the Universität Bern on StudySmarter:

What are some methods to reduce the sparsity of the user-item matrix in CBF?

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What are two different ways of forming a neighborhood of users in CBF?

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Name two different Top-N Recommendation strategies in CBF.

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What are two assumptions made in CBF methods?

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How many neighbors k should we consider in user-user CBF?

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Could we also make use of users that rate items very differently from the active user for predicting new items?

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What are some similaritiy metrics that can be used in user-user CBF?

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What is the difference between Pearson Correlation and Constrained Pearson Correlation?

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What are the advantages of item-item CBF over user-user CBF?

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Introduction To Recommender Systems

How does TF-IDF apply to CBF?

The TF-IDF concept can be used to create a profile of a document/object. A movie could be described as a weighted vector of its tags. These TF-IDF profiles can be combined with ratings to create user profiles, and then matched against future documents.

Introduction To Recommender Systems

What are advantages and disadvantages of content-based recommendation techniques?

Advantages:

  • Set of users can be small
  • Good for finding subsitutes
  • Good for finding the right product
  • Good explainability


Disadvantages:

  • Unlikely to find surprising connections
  • Harder to find complements (rather than substitutes)

Introduction To Recommender Systems

What is the difference between predictions and recommendations?

In the context of Recommender Systems, a prediction represents the anticipated opinion of a certain user for a given item. The predicted rating should have the same numerical scale as the real ratings.


A recommendation, on the other hand, is a list (subset) of all the items. The items in the list are chosen in a way that the user will probably like them the most of all the items in the system.

Introduction To Recommender Systems

What are some methods to reduce the sparsity of the user-item matrix in CBF?

  • Default Voting: insert default voting for unrated items, which corresponds to neutral or somewhat negative preference.
  • Preprocessing using Averages: for each user, assign his or her mean rating score to the unobserved items.
  • Filterbots: use (intelligent) automated rating agents to rate new items.
  • Dimensionality Reduction: use dimensionality reduction techniques like SVD. This may also solve the synonymy problem since the latent representation of two items with different names might be similar.

Introduction To Recommender Systems

What are two different ways of forming a neighborhood of users in CBF?

We can either just pick the useres that have the highest similarity with the active user or we can incrementally add a new user to the neighborhood, calculate the new centroid of this neighborhood and proceed by selecting the next user that is closest to this centroid.

Introduction To Recommender Systems

Name two different Top-N Recommendation strategies in CBF.

  • Recommend the N most frequent items that have been rated in the neighborhood of the active user but not by the active user itself.
  • Make use of association rules and choose the ones with the highest confidence.

Introduction To Recommender Systems

What are two assumptions made in CBF methods?

  • User tastes are stable
  • For a given domain (e.g. movies): if two users agree on one type of item, thy also agree on other types of items

Introduction To Recommender Systems

How many neighbors k should we consider in user-user CBF?

The lecture slides are somewhat ambivalent regarding this question. In general, seems to be often somewhere in between 25 and 100 in many domains, but it might be better to start with a value between 20 or 30 and 50. For movie ratings, Herlocker et al. found k = 20 to be a good value.

Introduction To Recommender Systems

Could we also make use of users that rate items very differently from the active user for predicting new items?

Yes. For example, if a user always rates things very low that the active user rates very high, we can infer that their tastes are somewhat orthogonal. We could use the bad ratings of this user to predict items that the active user is going to like.

Introduction To Recommender Systems

What are some similaritiy metrics that can be used in user-user CBF?

  • Pearson Correlation
  • Constrained Pearson Correlation
  • Spearman Rank Correlation
  • Cosine Similarity

Introduction To Recommender Systems

What is the difference between Pearson Correlation and Constrained Pearson Correlation?

In the Constrained Pearson Correlation, we use a neutral value instead of the mean rating of each user. This neutral value should denote neither agreement nor disagreement.

Introduction To Recommender Systems

What are the advantages of item-item CBF over user-user CBF?

  • Item-item similarity is fairly stable.
  • We can precompute the item-item similarity (since it is stable)
  • Faster recommendations for systems with a lot of users.


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