Introduction to Recommender Systems

<p>It may not accurately reflect user preferences (D)</p> Signup and view all the answers

<p>Includes user interactions such as clicks (C)</p> Signup and view all the answers

<p>They have limited predictive power for online user behavior. (B)</p> Signup and view all the answers

<p>It represents the square root of the mean of squared errors. (A)</p> Signup and view all the answers

<p>True positives identification. (A)</p> Signup and view all the answers

<p>Understanding the complexities of data involved. (A)</p> Signup and view all the answers

<p>MovieID 134 (D)</p> Signup and view all the answers

<p>They are interpreted as bad. (A)</p> Signup and view all the answers

<p>1 (C)</p> Signup and view all the answers

<p>Precision may increase. (B)</p> Signup and view all the answers

<p>Random assignment of users (A)</p> Signup and view all the answers

<p>Measuring accuracy of predictions. (D)</p> Signup and view all the answers

<p>MovieID 68 (C)</p> Signup and view all the answers

<p>150,000 (D)</p> Signup and view all the answers

<p>Negative ratings. (D)</p> Signup and view all the answers

<p>False negatives can often be too small. (D)</p> Signup and view all the answers

<p>In real-world scenarios and labs (A)</p> Signup and view all the answers

<p>Comparison of recommender methods (D)</p> Signup and view all the answers

<p>The average deviation between predicted ratings and actual ratings (B)</p> Signup and view all the answers

<p>It emphasizes larger deviations more than smaller ones. (C)</p> Signup and view all the answers

<p>Identifying a positively rated item as good (D)</p> Signup and view all the answers

<p>Evaluates the accuracy of the items that were recommended (C)</p> Signup and view all the answers

<p>The item is classified as good (C)</p> Signup and view all the answers

<p>Precision (D)</p> Signup and view all the answers

<p>RMSE squares the errors before averaging, while MAE takes absolute values. (C)</p> Signup and view all the answers

<p>An item was wrongly classified as bad when it was actually good. (B)</p> Signup and view all the answers

<p>If users had similar tastes in the past, they will have similar tastes in the future. (B)</p> Signup and view all the answers

<p>The ratings of similar users, adjusted by their similarity to the active user. (D)</p> Signup and view all the answers

<p>It serves as a weighting factor in the adjustment of rated versus average ratings. (C)</p> Signup and view all the answers

<p>To recommend the best-rated items to users based on similar tastes. (D)</p> Signup and view all the answers

<p>Using the active user’s overall rating habits combined with neighbor ratings. (D)</p> Signup and view all the answers

<p>A user’s rating history has no bearing on their future preferences. (B)</p> Signup and view all the answers

<p>Comparison of predicted ratings with actual user ratings. (A)</p> Signup and view all the answers

<p>When users have varied and unstable preferences. (A)</p> Signup and view all the answers

<p>Discover unknown items (C)</p> Signup and view all the answers

<p>Effective retrieval (B)</p> Signup and view all the answers

<p>Effective prediction (A)</p> Signup and view all the answers

<p>Optimize sales margins and profit (D)</p> Signup and view all the answers

<p>Convince or persuade users (A)</p> Signup and view all the answers

<p>Retrieval (D)</p> Signup and view all the answers

<p>Delivering unexpected but relevant items (D)</p> Signup and view all the answers

<p>Estimate user interest level (C)</p> Signup and view all the answers

<p>Sales margins and profit (C)</p> Signup and view all the answers

<p>Experience a pleasing browsing journey (C)</p> Signup and view all the answers

A system that suggests items to users based on their preferences and past behavior.

The goal of a recommendation system is to present items that the user is likely to be interested in, even if they haven't explicitly searched for them.

A recommendation system should provide users with items that are relevant to their needs and preferences.

A user knows what they want and searches for it explicitly.

A user doesn't know what they want or is exploring new options.

Predicting the likelihood of a user being interested in a specific item.

The goal of a recommendation system is to convince users to purchase or engage with the recommended items.

A user's positive reaction to recommendations, leading to engagement with the system.

The effectiveness of a recommendation system in achieving its goals.

A user model represents a user's preferences, behaviors, and other information relevant to the recommendation system.

Explicit feedback is when users directly express their preferences, like giving ratings on a scale of 1-5 or writing reviews.

Implicit feedback is when users' actions reveal their preferences, like clicking on a product or buying it.

Recommender Systems (RS) are designed to reduce information overload by using data to recommend relevant items to users.

Ratings are numerical values assigned by users to express their preferences for items.

User preferences are individual tastes, interests, and inclinations that inform their choices and actions.

A collaborative filtering technique that predicts a user's rating for an item by considering the ratings of similar users.

The assumption that users who had similar tastes in the past will likely have similar tastes in the future.

Calculating the similarity between users based on their shared ratings for items.

Finding the users whose ratings most closely resemble the active user's ratings.

The average rating of a user across all items they've rated.

The difference between a user's rating for an item and their overall average rating.

The weighted average of the rating deviations of the user's nearest neighbors for the unseen item, using the similarity between the active user and each neighbor as the weight.

Adding or subtracting the active user's average rating from the weighted average of their neighbors' deviations to adjust for individual rating tendencies.

The average absolute difference between predicted ratings and actual ratings.

The square root of the average squared difference between predicted ratings and actual ratings.

A method of evaluating recommender systems by comparing predicted ratings with actual ratings.

A method of evaluating recommender systems by deploying them in a real-world setting and observing their performance.

A factor that affects the success of a recommender system. It refers to the number of users interacting with the system and contributing data.

A factor that affects the success of a recommender system. It refers to the diversity of items recommended.

A factor that affects the success of a recommender system. It refers to the accuracy of the system's predictions.

A type of recommender system that focuses on the user's individual preferences.

A technique for converting rating data into binary data. This is done by applying a threshold to the original ratings. For example, all ratings above a certain value can be considered "good" and assigned a value of 1, while all ratings below are considered "bad" and assigned a value of 0.

A measure of the accuracy of a classification model. It calculates the proportion of relevant items retrieved out of all items retrieved. In recommendation systems, it represents the fraction of good recommendations among all recommendations given.

A measure of the completeness of a classification model. It calculates the proportion of relevant items retrieved out of all relevant items. In recommendation systems, it represents the proportion of good items that are recommended.

A classification metric borrowed from the field of Information Retrieval (IR). It is used to evaluate the quality of recommendations, treating the recommendation process as a retrieval task.

The degree to which a research finding can be applied to other situations or populations beyond the specific study.

The potential for a research finding to be practically useful and impactful in real-world settings.

A type of experimental research conducted in a controlled environment, often using simulated data or carefully selected participants. It is typically used to test specific hypotheses and assess the effectiveness of different approaches.

Research conducted in a real-world setting, using actual user data and behaviors. It allows for the evaluation of how a system performs in its intended environment.

A metric that measures the proportion of correctly predicted positive cases out of all positive cases. In recommendation systems, it refers to the ability to identify and suggest items that the user would actually like.

A metric that measures the proportion of correctly predicted positive cases out of all predicted positive cases. In recommendation systems, it refers to the ability to avoid recommending items that the user would not like.

A situation where the true preferences of users are unknown for items that were not recommended. This introduces uncertainty in evaluating the performance of recommendation systems.

A situation where the true preferences of users are unknown for items that were not recommended. This can lead to an overestimation of the system's effectiveness, as it assumes users dislike items they didn't rate.