OMRES-ProgressPresentation1.pptx
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The document proposes an online movie recommendation system (OMRES) that recommends movies to users based on movie details like overview, genres, cast, and crew. It discusses using a dataset of 5000 movies with details on budget, genres, and cast/crew. The proposed solution would take a user's input movie and find the most similar movies based on content-based filtering, comparing movie attributes. Content-based filtering is a widely used solution that analyzes user preferences through item content.
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As the development of information technology and Internet,
people enter the era of information overload from that of
information deficiency gradually[17]. The report is the result of
the Master Thesis in Information and Communication
Technology School at Royal Institute of Technology in
Stockholm, Sweden. The project is provided in VionLabs AB
that is a media-tech company providing media content such as
movie information for customers. The recommender system we
implemented is for the movie website.
During the past several decades personnel function has been
transformed from a relatively obscure record keeping staff to central
and top-level management function. important resources. This
project intends to introduce more user friendliness in the various
activities such all the details required for the correct statement
calculation and generation. as record updation, maintenance, and
searching. The searching of record has been made quite simple as all
the details of the customer can be obtained by simply keying in the
identification or account number of that customer. Similarly, record
maintenance and updation can also be accomplished by using the
account number with all the details being automatically generated.
These details are also being promptly automatically updated in the
master file thus keeping the record absolutely up-to-date. The entire
information has maintained in the database or Files and whoever wants to retrieve can't retrieve, only authorization user can retrieve
the necessary information which can be easily be accessible from the
file.
A computer-based recommendation system is designed to handle
all the primary information required to calculate user's emotions.
Separate database is maintained to handle all the details required
for the correct statement calculation and generation.
This project intends to introduce more user friendliness in the
various activities such as record updation, maintenance, and
searching. The searching of record has been made quite simple
as all the details of the customer can be obtained by simply
keying in the identification or account number of that customer.
Similarly, record maintenance and updation can also be
accomplished by using the account number with all the details
being automatically generated. These details are also being
promptly automatically updated in the master file thus keeping
the record absolutely up-to-date.
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of customers facility, the main objective of this system is to find
out the actual customer service. Etc. It should fulfill almost all
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User-based and Item-based collaborative-filtering algorithms are all neighborhood-
based algorithm, there are also a lot of other collaborative-filtering algorithms. Hoff-
man raised Latent Class Model in this paper[13], the model connects user and item
by latent class, which considers that a user will.
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people enter the era of information overload from that of
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the Master Thesis in Information and Communication
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Stockholm, Sweden. The project is provided in VionLabs AB
that is a media-tech company providing media content such as
movie information for customers. The recommender system we
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project intends to introduce more user friendliness in the various
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calculation and generation. as record updation, maintenance, and
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identification or account number of that customer. Similarly, record
maintenance and updation can also be accomplished by using the
account number with all the details being automatically generated.
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information has maintained in the database or Files and whoever wants to retrieve can't retrieve, only authorization user can retrieve
the necessary information which can be easily be accessible from the
file.
A computer-based recommendation system is designed to handle
all the primary information required to calculate user's emotions.
Separate database is maintained to handle all the details required
for the correct statement calculation and generation.
This project intends to introduce more user friendliness in the
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searching. The searching of record has been made quite simple
as all the details of the customer can be obtained by simply
keying in the identification or account number of that customer.
Similarly, record maintenance and updation can also be
accomplished by using the account number with all the details
being automatically generated. These details are also being
promptly automatically updated in the master file thus keeping
the record absolutely up-to-date.
The main objective of our project is providing the different typed
of customers facility, the main objective of this system is to find
out the actual customer service. Etc. It should fulfill almost all
the process requirements of any recommendation system.
User-based and Item-based collaborative-filtering algorithms are all neighborhood-
based algorithm, there are also a lot of other collaborative-filtering algorithms. Hoff-
man raised Latent Class Model in this paper[13], the model connects user and item
by latent class, which considers that a user will.
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This document outlines a movie recommendation system project built using collaborative filtering. The project aims to build a recommendation engine that suggests movies to users based on their preferences and watching history. It will use the MovieLens dataset and implement item-based collaborative filtering. The key steps include importing libraries, preprocessing the data, building the recommendation model using collaborative filtering, and evaluating the model's performance. Collaborative filtering works by comparing a user's preferences to other users to find patterns and provide personalized recommendations. The document also discusses some disadvantages of collaborative filtering like the cold-start problem and difficulty including additional metadata.
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学校原版美国波士顿大学毕业证学历学位证书原版一模一样
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An improved modulation technique suitable for a three level flying capacitor ...
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Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
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- 1. Online Movie Recommendation System (OMRES) Sohel Kazi 03496202719 Hridik Gulati 04596202719
- 2. Content • Introduction • Problem Statement • Background • Proposed Solution • Conclusion
- 3. Introduction • People seek information via words, recommendation letters, news reports etc. • Recommendation systems imitate this social process to enable quick filtering of the information on the web • Lots of companies try to offer services that involve recommendations to address the right user groups.
- 4. Problem Statement ● Providing related content out of relevant and irrelevant collection of items to users of online service providers. ● OMRES (Online Movie Recommendation System) aims to recommend movies to users based on movie overview, genres, cast and crew.
- 5. Problem Statement ● Given a set of movies database giving similar movies recommendations to the users based on user movie input having maximum similarities based on genre, cast and crew. ● Ex:- “If the user movie input is Avatar than the recommendations would be similar to it based on given criteria which could be i. e. Aliens.”
- 6. Background (Dataset) ● Tmdb 5000 Movie dataset that provides 5000 movies details like budget, genres, homepage, ID, keywords, popularity and many more details. ● Tmdb 5000 credits dataset that provides 5000 movies details like title, cast and crew. ● Each movie is represented by a unique ID.
- 7. Problem Background • Earlier systems implemented in 1990s. o GroupLens (Usenet articles) [Resnick, 1997] o Siteseer (Cross linking technical papers)[Resnick, 1997] o Tapestry (email filtering) [Goldberg, 1992] • Earlier solutions provided for users to rate the item.
- 8. Problem Background • Item-to-item collaborative filtering (people who buy x also buy y), an algorithm popularized by Amazon.com's recommender system • Last.fm recommends music based on a comparison of the listening habits of similar users • Facebook, MySpace, LinkedIn, and other social networks use collaborative filtering to recommend new friends
- 9. Background For the Solution • Content Based Filtering o Selects items based on the correlation between the content of the items and the user’s preferences o Compare user profile to content of each item • Collaborative Filtering o chooses items based on the correlation between people with similar preferences. o Rate items based on ratings of the users that rated the same items
- 10. Background For the Solution • There are 3 types of CF • Memory-based • Model-based • Hybrid o Memory based and Model-based o Content Based and Collaborative Filtering
- 16. Conclusion • Recommendation systems provide content for us by taking user input movie and finding the most similar movies based on the content. • Content-based Filtering is a widely used solution for this problem which we make use of in our project
- 17. References ● https://www.kaggle.com/datasets/tmdb/tmd b-movie metadata?select=tmdb_5000_movies.csv ● Nearest Neighbor Approach using Clustering on the TMDB 5000 data.
- 18. Questions