Item-based collaborative filtering technique for movie recommender based on user preference - Wong, Wai Leong (2018)
With the advent of web 2.0, most of the people start using internet to do the thing for example purchase product through online, online booking stuff and we can say that with internet, it bring convenient to our living. Although internet bring so much convenient for us but there are some problem occur when there are too many choices can be choose on internet. This problem will caused dilemma happened on user. Besides, with the help of the recommender system, user can noticed and enjoyed the movie according to their preference and time consuming while searching the movie will be reduced. Therefore, item-based collaborative filtering technique is apply on online movie in order to analyses the relationship between user and movie based on the rating and tagging provided by user. Item-based collaborative filtering technique is suitable used in movie recommender while generate recommendation to user because it calculate the similarity between movie and movie. The proposed schema will be implement in python language on Anaconda and result will be evaluated in the end of research. The final result of this research is top-10 movies will be generated for user based on the highest rating provided by the previous user. For new user, he/she can enter the type of the movie they may like in the column. After that, another top-10 movies list will be generated for the new user based on the specific preference of the particular user.