Improving Web Movie Recommender System Based on Emotions
نویسندگان
چکیده
Recommender Systems (RSs) are garnering a significant importance with the advent of e-commerce and ebusiness on the web. This paper focused on the Movie Recommender System (MRS) based on human emotions. The problem is the MRS need to capture exactly the customer’s profile and features of movies, therefore movie is a complex domain and emotions is a human interaction domain, so difficult to combining together in the new Recommender System (RS). In this paper, we prepare a new hybrid approach for improving MRS, it consists of Content Based Filtering (CBF), Collaborative Filtering (CF), emotions detection algorithm and our algorithm, that presented by matrix. The result of our system provides much better recommendations to users because it enables the users to understand the relation between their emotional states and the recommended movies. Keywords—movie recommender system; collaborative filtering; content based filtering; emotion; CF; CBF; MRS
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