Movie Recommendation Application Study Based On Collaborative Filtering Algorithm

With the rapid development of e-commerce, personalized recommendation used for theexploration of every customer’s potential demands plays a more and more important role.Collaborative filtering recommendation method is one of the most popular personalizedrecommendation methods. There are also some bottlenecks to the application of this method,such as scalability, sparseness and cold boot. In this paper, a research on adoptingcollaborative filtering algorithm was made to carry out personalized recommendation.In this paper, users’ preference for various movies, which varies in degree, is analyzed.On giving the ratings with slope-one, the item category data is taken into full account. Theratings of movies in the same category are used to find similar users and to predict the ratings,which effectively solves the problem that users’preference differs for movies in differentcategories.The collaborative filtering recommendation system has to search for the nearest neighborof the target user in the entire user space. As the data constantly increases and the scale of thesystem continuously expands, the amount of computation tends to increase linearly forsearching the nearest neighbor of the target users in the whole user space, which will result inworse real-time performance of the system. This has become the bottleneck for thedevelopment of the collaborative filtering system. Aiming at this problem, a collaborativefiltering algorithm based on clustering is adopted in this paper. Users with similar interests arecategorized into one group based on the users’ attributive characters and users’ rating data. Assoon as a target user arrives, he or she should be put into the appropriate cluster. Then, thenearest neighbor of the target user is searched in the corresponding cluster, to look for thenearest neighbor of the target user in a smaller user space and to shorten the computation timespent in searching the nearest neighbor.In this paper, the recommendation of new items and new users is also taken into consideration: the movies in the existing database are clustered according to category attributeand ratings, to gather the movies similar in category and ratings into a cluster. With regard tonew movies, a new movie is classified into the cluster it belongs to, according to its basicfeatures. In the cluster, on the basis of the collaborative filtering algorithm, the ratings thetarget users may give to the new movie are predicted and then a recommendation list isgenerated. For new users, they are classified into the right cluster based on the basiccharacteristics of such users. And the user’s nearest neighbor is searched in the cluster. Byusing the collaborative filtering algorithm, the rating that the user may give to the target itemis predicted, and a recommendation list is generated.In order to verify the validity of the algorithm stated that this paper adopted, a test wascarried out by using data released on MovieLens, and a detailed analysis was made on the testresults.