Content-Based Movie Recommendation System Using MBO with DBN

The content-basedfiltering technique has been used effectively in a variety of Recommender Systems(RS).The user explicitly or implicitly provides data in the Content-Based Recommender System.The system collects this data and creates a profile for all the users,and the recommendation is generated by the user profile.The recommendation generated via content-basedfiltering is provided by observing just a single user’s profile.The primary objective of this RS is to recommend a list of movies based on the user’s preferences.A con-tent-based movie recommendation model is proposed in this research,which recommends movies based on the user’s profile from the Facebook platform.The recommendation system is built with a hybrid model that combines the Mon-arch Butterfly Optimization(MBO)with the Deep Belief Network(DBN).For feature selection,the MBO is utilized,while DBN is used for classification.The datasets used in the experiment are collected from Facebook and MovieLens.The dataset features are evaluated for performance evaluation to validate if data with various attributes can solve the matching recommendations.Eachfile is com-pared with features that prove the features will support movie recommendations.The proposed model’s mean absolute error(MAE)and root-mean-square error(RMSE)values are 0.716 and 0.915,and its precision and recall are 97.35 and 96.60 percent,respectively.Extensive tests have demonstrated the advantages of the proposed method in terms of MAE,RMSE,Precision,and Recall compared to state-of-the-art algorithms such as Fuzzy C-means with Bat algorithm(FCM-BAT),Collaborativefiltering with k-NN and the normalized discounted cumulative gain method(CF-kNN+NDCG),User profile correlation-based similarity(UPCSim),and Deep Autoencoder.

Personalized Real-Time Movie Recommendation System:Practical Prototype and Evaluation

With the eruption of big data,practical recommendation schemes are now very important in various fields,including e-commerce,social networks,and a number of web-based services.Nowadays,there exist many personalized movie recommendation schemes utilizing publicly available movie datasets(e.g.,MovieLens and Netflix),and returning improved performance metrics(e.g.,Root-Mean-Square Error(RMSE)).However,two fundamental issues faced by movie recommendation systems are still neglected:first,scalability,and second,practical usage feedback and verification based on real implementation.In particular,Collaborative Filtering(CF)is one of the major prevailing techniques for implementing recommendation systems.However,traditional CF schemes suffer from a time complexity problem,which makes them bad candidates for real-world recommendation systems.In this paper,we address these two issues.Firstly,a simple but high-efficient recommendation algorithm is proposed,which exploits users1 profile attributes to partition them into several clusters.For each cluster,a virtual opinion leader is conceived to represent the whole cluster,such that the dimension of the original useritem matrix can be significantly reduced,then a Weighted Slope One-VU method is designed and applied to the virtual opinion leader-item matrix to obtain the recommendation results.Compared to traditional clusteringbased CF recommendation schemes,our method can significantly reduce the time complexity,while achieving comparable recommendation performance.Furthermore,we have constructed a real personalized web-based movie recommendation system,MovieWatch,opened it to the public,collected user feedback on recommendations,and evaluated the feasibility and accuracy of our system based on this real-world data.

A Novel Approach Based on Multi-View Content Analysis and Semi-Supervised Enrichment for Movie Recommendation

Although many existing movie recommender systems have investigated recommendation based on information such as clicks and tags, much less efforts have been made to explore the multimedia content of movies, which has potential information for the elicitation of the user＇s visual and musical preferences. In this paper, we explore the content from three media types （image, text, audio） and propose a novel multi-view semi-supervised movie recommendation method, which represents each media type as a view space for movies. The three views of movies are integrated to predict the rating values under the multi-view framework. ~rthermore, our method considers the casual users who rate limited movies. The algorithm enriches the user profile with a semi-supervised way when there are only few rating histories. Experiments indicate that the multimedia content analysis reveals the user＇s profile in a more comprehensive way. Different media types can be a complement to each other for movie recommendation. And the experimental results validate that our semi-supervised method can effectively enrich the user profile for recommendation with limited rating history.

A novel movies recommendation algorithm based on reinforcement learning with DDPG policy

Purpose-English original movies played an important role in English learning and communication.In order to find the required movies for us from a large number of English original movies and reviews,this paper proposed an improved deep reinforcement learning algorithm for the recommendation of movies.In fact,although the conventional movies recommendation algorithms have solved the problem of information overload,they still have their limitations in the case of cold start-up and sparse data.Design/methodology/approach-To solve the aforementioned problems of conventional movies recommendation algorithms,this paper proposed a recommendation algorithm based on the theory of deep reinforcement learning,which uses the deep deterministic policy gradient(DDPG)algorithm to solve the cold starting and sparse data problems and uses Item2vec to transform discrete action space into a continuous one.Meanwhile,a reward function combining with cosine distance and Euclidean distance is proposed to ensure that the neural network does not converge to local optimum prematurely.Findings-In order to verify the feasibility and validity of the proposed algorithm,the state of the art and the proposed algorithm are compared in indexes of RMSE,recall rate and accuracy based on the MovieLens English original movie data set for the experiments.Experimental results have shown that the proposed algorithm is superior to the conventional algorithm in various indicators.Originality/value-Applying the proposed algorithm to recommend English original movies,DDPG policy produces better recommendation results and alleviates the impact of cold start and sparse data.