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Recommender System 推荐系统

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CSE 158, CSE 258, DSC 256, MGTA 461 Web Mining and Recommender Systems, Fall 2023 : Homework 3 - Play prediction
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Although we have built a validation set, it only consists of positive samples. For this task we also need examples of user/item pairs that weren’t played.
CSE 158, CSE 258 Web Mining and Recommender Systems, Fall 2023 : Homework 4 - Text Mining
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Using the Steam category data, build training/test sets consisting of 10,000 reviews each. Code to do so is provided in the stub.1 We’ll start by building features to represent common words
CSE 158, CSE 258, DSC 256, MGTA 461 Web Mining and Recommender Systems, Fall 2023 : Assignment 2
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Identify a dataset to study, and perform an exploratory analysis of the data. Describe the dataset, including its basic statistics and properties, and report any interesting findings.
CSE 158, CSE 258, DSC 256, MGTA 461 Web Mining and Recommender Systems, Fall 2023 : Assignment 1: Video Game Prediction
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In this assignment you will build recommender systems to make predictions related to video game reviews from Steam.
[2022] UNSW - COMP9727 Recommender Systems - Assignment 1 Recommendation Algorithm Implementation
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Introduction In this assignment, you will be required to manually implement a few recommendation algorithms in Python as well as answer some corresponding questions individually.
COMPSCI 753 Algorithms for Massive Data - Semester2 2021- Final Exam - Q4 Recommender Systems
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A recommender system generates a ranked list of items for a specific user u as (p3, p10, p5, p7, p1, p9, p2, p4, p6, p8). The ranked list contains all items that haven’t been purchased by the user in the training data. Apply the basic user-based collaborative filtering (without considering bias) with cosine similarity. Give the top-1 recommended item to user u2.
INT303 Big Data Analytics - Final Exam: Question 7: Recommender Systems
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Consider a dataset containing information about movies: genre, director and release decade. We also have information about which users have seen each movie. The rating for a user on a movie is either 0 or 1.
HW4: implementing item-based CF with cosine
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First, run recommenderDemo.ipynb and be familar with the code and data. Second, implement item-based CF with cosine
Midterm: Recommender System for Movies
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In this project, you will implement a recommender system for your classmates, professor and TAs based on the movie survey we have conducted.
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