Abstract
In today's competitive world, the rapid advancement of technology, Internet and electronic commerce raise demand for existence of a mechanism which can predict user’s requirements and requests. These mechanisms can lead us to outsource our competitors. The main issue is that we encounter large amount of information in web portals which are mostly heterogeneous and unrelated, therefore with no proper strategies of categorizing data and information preparation, users get involved in confusion accessing correct content. The most important challenges are reaching most relevant information in order to provide users. As a matter of fact, this problem could get solved by using domain of recommender systems which can help us finding and selecting related information according to user needs. Although, recommender systems help people dealing with massive data, these systems are less employed on Web portals. Certainly, the application of these types of systems on web portals will bring decent improvements to users. The study, uses MovieLens20m dataset that includes ratings and user labels for movies. User ratings and movie rating relationships are used in order to make an appropriate recommendation for users. labels which users considered for the movies are also employed in extraction phase. Finally, a combination of these two categories is offered as a recommendation to user. Keywords: Recommender Systems, Web Portals, User Visits, MovieLens DOI : 10.7176/JIEA/9-3-03 Publication date :May 31 st 2019
Highlights
The electronic world is moving towards information saturation since over the past decade, a large amount of data has been stored on data servers and databases
Recommender systems attempt to identify most applicable and closest item to user based on their attitude and perception with the help of information obtained from his behavior in relation to other similar users
A method has been proposed for recommender systems which mainly applies information retrieval techniques on a movie dataset consisting of two sections
Summary
The electronic world is moving towards information saturation since over the past decade, a large amount of data has been stored on data servers and databases. [3,4,5,6] They extract user's past behavior, even services and information that he did not notice, in order to predict and suggest interesting results. These systems are one of the main tools of overcoming information accumulation. Considering the position of this system in today's eworld and taking into account user’s expectations to access correct information in the right place at the right time, researchers have introduced recommender systems a tool for empowering users in order to find qualified services with optimal manner [6,7,8]
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