The World Wide Web has brought numerous changes in the way we live and communicate others. Today, we rely on Web and its related technologies like web search engines that search and retrieve information on almost any aspect of life. However, this change results information overload problem. To cope
Recommender Systems
The World Wide Web has brought numerous changes in the way we live and communicate others. Today, we rely on Web and its related technologies like web search engines that search and retrieve information on almost any aspect of life. However, this change results information overload problem. To cope with these issues the recommender systems works as a helper in finding relevant and related information to the users. These systems navigate the web to come up with relevant suggestions for users based either on their explicitly mentioned preferences or objective behaviors. Recommender systems are used in various domains including videos, images, products, articles, news and books. The demand of this system is growing with the passage of time, thus a reliable and high quality recommender systems are required to meet the requirements of users.
There are three main methods used in recommender systems namely, Content Based Filtering, Collaborative Filtering and Hybrid Filtering. Content based systems works on the user explicitly mentioned preferences, while Colloborative based systems trace the previous records or more precisely history to suggets relevant information to the users. Both systems comes with its pros and cons, in some situations one get priority over other. To overcome the issues faced by CB and CF methods the concept of Hybrid method is used, which uses the ingredients of both CB and CF. There is plenty of advantages of recommender systems but as a fact every systems come up with some problems or disadvantages, there are following problems faced by recommender systems that needs to solved by a prominant solution, Cold Start, Synonymy, Shilling Attacks, Privacy, Limited Content Analysis and overspecialization, Grey Sheep, Sparsity, Scalibility, Latency Problem, Context Awareness, and Availability of Online Datasets.
The objective of my research is to cope above mentioned issued faced by recommender systems. The researchers are working to cope these issues and have already solved a bunch of problems. My main focus is to present a recommender system that is free of such issues and to target the area in which no progress has been made. There are plenty of problems that needs to be solved through recommender system. The aim of my research is to solve a real world problem that has been face by users.
Implementation is essential part of research, after evaluating my paper(s) on recommender system, I will implement my research in some papular and advance programming language, the final product of my research will be a web or mobile plugin that will suggets relevant information to the users. The implemented product will not only be a simple software but an intelligent one, it will be implemented with the help of advance techniques provide by artifcial intelligence namely, Deep learning, Natural Language Processing etc.
With the growth of technologies the information is also growing with high rate. There are millions of information available on the web which results information overload problem and many others. My aim is to present a reliable system that benefits the users. There are following benefits of my research/project.
There is less technicalities in my research, the requirements can be gathered or covered from my research. In the last a software will be presented in the form of plugin.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Papers | Miscellaneous | 2500 | 2 | 5000 |
| Binding | Miscellaneous | 5 | 1000 | 5000 |
| Total in (Rs) | 10000 |
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