Dynamic Personalized Movie Recommendation System

ABSTRACT:

Recommendation techniques are very important in the fields of E-commerce and other Web-based services. One of the main difficulties is dynamically providing high-quality recommendation on sparse data. In this paper, a novel dynamic personalized recommendation algorithm is proposed, in which information contained in both ratings and profile contents are utilized by exploring latent relations between ratings, a set of dynamic features are designed to describe user preferences in multiple phases, and finally a recommendation is made by adaptively weighting the features. Experimental results on public datasets show that the proposed algorithm has satisfying performance.

PROJECT OUTPUT VIDEO:

EXISTING SYSTEM:

There are mainly three approaches to recommendation engines based on different data analysis methods, i.e., rule-based, content-based and collaborative filtering. Among them, collaborative filtering (CF) requires only data about past user behavior like ratings, and its two main approaches are the neighborhood methods and latent factor models. The neighborhood methods can be user-oriented or item-oriented. They try to find like-minded users or similar items on the basis of co-ratings, and predict based on ratings of the nearest neighbors.

DISADVANTAGES OF EXISTING SYSTEM:

Proper content cannot be delivered quickly to the appropriate customers.
No accurate prediction / Recommendation.
Involve most ratings to capture the general taste of users, they still have difficulties in catching up with the drifting signal in dynamic recommendation because of sparsity, and it is hard to physically explain the reason of the involving.

PROPOSED SYSTEM:

In this paper, we present a novel hybrid dynamic recommendation approach. Firstly, in order to utilize more information while keeping data consistency, we use user profile and item content to extend the co-rate relation between ratings through each attribute, as shown in figure.

The main contributions of this paper can be summarized as follows:

(a) More information can be used for recommender systems by investigating the similar relation among related user profile and item content.

(b) A novel set of dynamic features is proposed to describe users’ preferences, which is more flexible and convenient to model the impacts of preferences in different phases of interest compared with dynamic methods used in previous works, since the features are designed according to periodic characteristics of users’ interest and a linear model of the features can catch up with changes in user preferences.

(c)An adaptive weighting algorithm is designed to combine the dynamic features for personalized recommendation, in which time and data density factors are considered to adapt with dynamic recommendation on sparse data.

ADVANTAGES OF PROPOSED SYSTEM:

Hybrid dynamic recommendation approach.
Effective with dynamic data and significantly outperforms previous algorithms.
Accurate predication and Recommendation.
More information can be used for recommender systems by investigating the similar relation among related user profile and item content.

MODULES:

1) User Profile Module

2) User Ratings Module

3) Similarity Computation Module

4) Dynamic Personalized Recommendation Module

MODULES DESCRIPTION:

1) User Profile Module

In this module, we collect user profile information such as Name, age, gender etc. To evaluate and propose our model we develop online movie recommender services. In this online movie recommender services consists of admin and User modules. Where the admin can upload the movies, with their details. Can view user details. Can delete the movies etc. User has to register first to access the recommendation model. After registering user gets access to the system, where all the movies information are updates.

2) User Ratings Module

Users’ preferences or items’ reputations are drifting, thus we have to deal with the dynamic nature of data to enhance the precision of recommendation algorithms, and recent ratings and remote ratings should have different weights in the prediction.

So we propose a set of dynamic features to describe users’multi-phase preferences in consideration of computation, flexibilityand accuracy. It is impossible to learn weights of allratings for each user, but it is possible to learn the generalweights of ratings in the user’s different phases of interestif the phases include ranges of time that are long enough. In this module, user can rate to the movies by clicking the movie which they interested.

3) Similarity Computation Module

Users’ preferences or items’ reputations are drifting, thuswe have to deal with the dynamic nature of data to enhancethe precision of recommendation algorithms, and recentratings and remote ratings should have different weights inthe prediction.

For the sparsity of recommendation data, the main difficulty of capturing users’ dynamic preferences is the lack of useful information, which may come from three sources – user profiles, item profiles and historical rating records. Traditional algorithms heavily rely on the co-rate relation (to the same item by different users or to different items by the same user), which is rare when the data is sparse. Useful ratings are discovered using the co-rate relation, which is simple, intuitional and physically significant when we go one or two steps along, but it strongly limits the amount of data used in each prediction.

4) Dynamic Personalized Recommendation Module

More information can be used for recommender systems by investigating the similar relation among related user profile and item content. We proposed a novel dynamic personalized recommendation algorithm for sparse data, in which more rating data is utilized in one prediction by involving more neighboring ratings through each attribute in user and item profiles. A set of dynamic features are designed to describe the preference information based on TSA technique, and finally a recommendation is made by adaptively weighting the features using information in multiple phases of interest.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor
Hard Disk : 500 GB..
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
RAM : 4 GB.

SOFTWARE REQUIREMENTS:

Operating system : Windows 10/11.
Coding Language : C#.net.
Frontend : ASP.Net, HTML, CSS, JavaScript.
IDE Tool : VISUAL STUDIO.
Database : SQL SERVER 2005.

Dotnet Projects

Dynamic Personalized Movie Recommendation System