The explosive growth in the amount of available digital information and the number of visitors to the Internet have created a potential challenge of information overload which hinders timely access to items of interest on the Internet. This has increased the demand for recommender systems more than ever before . Recommender systems are information filtering systems that deal with the problem of information overload by filtering vital information fragment out of large amount of dynamically generated information according to user’s preferences, interest, or observed behaviour about item. Recommender system has the ability to predict whether a particular user would prefer an item or not based on the user’s profile.
So, Basically Recommendation system is a type of software that keeps feeding on users data to develop a trend for users preferences ,interests and usage behaviour of a particular software. There are six types of recommender systems which work primarily in the Media and Entertainment industry: Collaborative Recommender system and Content-based recommender system. Now if we come to the most basic use of recommendation system it would be word recommendation.
Our society in getting more and more advanced so, we require a software to type faster and effectively and also to find new words and learn them.
This is what our software does, it learns from user’s choices and recommends accordingly, if user want to explore new words, the user can explore them in dictionary.
Phase1: Gathering information about different recommendation systems.
Phase2: Selecting the appropriate recommendation system for our project.
Phase3: Writing code for the project and implementation of content based filtering.
Phase4: Implementation of priority queue in a file.
Phase5: Testing and running of project.
This project discusses , how a recommendation system works using word recommendation system.
Developed a Content Based Recommending System using C programming language to demonstrate the concepts of Word Prediction and Recommendation and how content based recommending system works.
Content Based Filtering Algorithm :
Content-based filtering uses item features to recommend other items similar to what the user likes, based on their previous actions or explicit feedback.
Based on that data, a user profile is generated, which is then used to make suggestions to the user.
As the user provides more inputs or takes actions on the recommendations, the engine becomes more and more accurate.
Bubble Sort :
Bubble sort is a simple sorting algorithm that repeatedly steps through the list, compares adjacent elements and swaps them if they are in the wrong order.
In our program it is used to sort the priority queue according to priority.
Linear searching :
Linear searching is used when we want to search each and every element of the list to find the correct match.
Recommender systems open new opportunities of retrieving personalized information on the Internet. It also helps to alleviate the problem of information overload which is a very common phenomenon with information retrieval systems and enables users to have access to products and services which are not readily available to users on the system. Word prediction techniques have been frequently designed with the aim to accelerate the typing speed, increase the communication rate and to reduce the effort needed to type a text. These techniques have been initially included in aids for the people with motor disabilities, improves the quality of life, but non-disabled people can also use them while composing messages to provide more comfort and spelling assistance.
This recommendation system tried to combine the existing algorithms for recommendation. Recommender systems make the selection process easier for the users. This recommender system will assuredly be a great web application, which can be clubbed with today’s high demanding online purchasing web sites. Our approach can be extended to various domains to recommend books, music, etc.
Star it on github !