Movie Recommendations via Item-Item Collaborative Filtering. You are provided with real-data (Movie-Lens dataset) of user ratings for different movies. There is a readme file that describes the data format. In this project, you will implement the item-item collab- orative filtering algorithm that we discussed in the class. The high-level steps are as follows:
Movie Recommendations via Item-Item Collaborative Filtering. You are provided with real-data (Movie-Lens dataset) of user ratings for different movies. There is a readme file that describes the data format. In this project, you will implement the item-item collab- orative filtering algorithm that we discussed in the class. The high-level steps are as follows:
Computer Networking: A Top-Down Approach (7th Edition)
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ISBN:9780133594140
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Chapter1: Computer Networks And The Internet
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Types of Linked List
A sequence of data elements connected through links is called a linked list (LL). The elements of a linked list are nodes containing data and a reference to the next node in the list. In a linked list, the elements are stored in a non-contiguous manner and the linear order in maintained by means of a pointer associated with each node in the list which is used to point to the subsequent node in the list.
Linked List
When a set of items is organized sequentially, it is termed as list. Linked list is a list whose order is given by links from one item to the next. It contains a link to the structure containing the next item so we can say that it is a completely different way to represent a list. In linked list, each structure of the list is known as node and it consists of two fields (one for containing the item and other one is for containing the next item address).
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Movie Recommendations via Item-Item Collaborative Filtering. You are provided
with real-data (Movie-Lens dataset) of user ratings for different movies. There is a readme
file that describes the data format. In this project, you will implement the item-item collab-
orative filteringalgorithm that we discussed in the class. The high-level steps are as follows:
with real-data (Movie-Lens dataset) of user ratings for different movies. There is a readme
file that describes the data format. In this project, you will implement the item-item collab-
orative filtering
a) Construct the profile of each item (i.e., movie). At the minimum, you should use the
ratings given by each user for a given item (i.e., movie). Optionally, you can use other in-
formation (e.g., genre information for each movie and tag information given by user for each
movie) creatively. If you use this additional information, you should explain your method-
ology in the submitted report.
b) Compute similarity score for all item-item (i.e., movie-movie) pairs. You will employ the
centered cosine similarity metric that we discussed in class.
c) Compute the neighborhood set Nfor each item (i.e. movie). You will select the movies
that have highest similarity score for the given movie. Please employ a neigborhood of size
5. Break ties using lexicographic ordering over movie-ids.
d) Estimate the ratings of other users who didn’t rate this item (i.e., movie) using the neigh-
borhood set. Repeat for each item (i.e., movie).
borhood set. Repeat for each item (i.e., movie).
e) Compute the recommended items (movies) for each user. Pick the top-5 movies with
highest estimated ratings. Break ties using lexicographic ordering over movie-ids.
Your program should output top-5 recommendations for each user
Please Answer in python
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