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FIT5196 Data wrangling - assessment 1: Extracting data from semi-structured text files

FIT5196Data wranglingPythonData Extraction

Text documents, such as crawled web data, are usually composed of topically coherent text data, which within each topically coherent data, one would expect that the word usage demonstrates more consistent lexical distributions than that across the dataset. A linear partition of texts into topic segments can be used for text analysis tasks, such as passage retrieval in IR (information retrieval), document summarization, recommender systems, and learning-to-rank methods.

CSE 158, CSE 258, DSC 256, MGTA 461 Web Mining and Recommender Systems, Fall 2023 : Homework 3 - Play prediction

CSE 158CSE 258DSC 256MGTA 461Web Mining and Recommender SystemsPlay prediction

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

CSE 158CSE 258Web Mining and Recommender SystemsText MiningBag-of-wordsWord2vec

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

CSE 158CSE 258DSC 256MGTA 461Web Mining and Recommender Systems

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

CSE 158CSE 258DSC 256MGTA 461Web Mining and Recommender SystemsVideo Game Prediction

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

COMP9727Recommender SystemsPythonAlgorithmAssociate RuleLatent Factor Method

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

Algorithms for Massive DataAuckland澳洲Recommender Systems

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

INT303Big Data AnalyticsHadoopRecommender Systems

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

RecommendationRecommender SystemUSWPIWorcester Polytechnic Institute

First, run recommenderDemo.ipynb and be familar with the code and data. Second, implement item-based CF with cosine

Midterm: Recommender System for Movies

Recommender SystemRecommendation

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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