Published on Sep 03, 2023
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.
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.
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.
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.
Hybrid dynamic recommendation approach.
Effective with dynamic data and significantly outperforms previous algorithms.
Accurate predication and Recommendation.
More information can be used for similar relation among related user profile and item content.
Adding New Movie Details
User Registration & Login
Recommended based on Age and Gender
User Rating for Movies
In this application admin can only add new movies details to search and rated by user. Admin need to login into this system by providing his/her username and password, after successful login admin can add the new movies details such as Movie name, release language, crew and cast, genre of the movies and the poster of the particular movie. These details are important to search by the user. The movies are search by their title name and/or which genre the movie belongs to.
To search and rate any movie in this application all user must registered with the system. User must register with this application for access the movie list uploaded by the admin. User registers in this application with their details such as Name, gender, age, mobile number, email, username and password. After successful registration user able to login into the system then search for a movie and rate any movie based on the satisfaction they realized in that movie.
After successful login user can search for a particular movie and rate the movie. The rating is based on the 5 Star Rating. If the user give 5 star to a movie it’s 100% satisfied the user, and if any user give 1 Star for a movie then it’s not up to user’s expectations. Based on the user rating the movie is recommended to other user in the same genre or in same age group. This rating is very important for the system to maintain the high rated and mostly liked movies and recommend it to other user of this application.
After successful login user can search for a particular movie and rate the movie. User can also see the recommended movies based the ratings already given by other user. The recommendation is based on the age and gender of the users already rated. For example if the user is Male then the movies are recommended which are most rated by male user already rated. And if the user is in Young age group then the movies which are mostly rated by same age group will be recommended to the user. User have the option to filter the recommended based any one of the Criteria Age or Gender.
CPU type : Pentium IV 2.4 GHz.
Clock speed : 3.0 GHz
Ram size : 512 MB
Hard disk capacity : 40 GB
Monitor type : 15 Inch color monitor
Keyboard type : internet keyboard
Mobile : ANDROID MOBILE
Operating system : Windows XP.
Coding Language : Java 1.6
Tool Kit : Android 2.2
IDE : Eclipse
