Alleviation of Cold Start in Movie Recommendation Systems using Sentiment Analysis of Multi-Modal Social Networks

Subject Areas : Data Mining

Mehrnaz Mirhasani ¹ , Reza Ravanmehr ²

1 - Computer Engineering Department, Central Tehran Branch, Islamic Azad University,
2 - Computer Engineering Department, Central Tehran Branch, Islamic Azad University,

Received: 2020-04-26 Accepted : 2020-12-22 Published : 2020-11-01

Keywords: Sentiment Analysis, Mojo Box office, Cold Start, IMDB, Movie Recommendation Systems, Twitter,

Abstract :

The movie recommendation systems are always faced with the new movie cold start problem. Nowadays, social media platform such as Twitter is considered as a rich source of information in various domains, like movies, motivated us to exploit Twitter's content to tackle the movie cold start problem. In this study, we propose a hybrid movie recommendation method utilizing microblogs, movie features, and sentiment lexicon to reduce the effect of data sparsity. For this purpose, first, the movie features are extracted from the Internet Movie Database (IMDB), and the average IMDB score is calculated during the 7-days opening of the movie. Second, the related tweets of the movie and the cast are retrieved by the Twitter API. Third, the polarity of tweets and the public’s feeling towards the target movie is extracted using sentiment lexicon analysis. Finally, the results of the three previous steps are integrated, and the prediction is obtained. Our results are compared with the sales volume of the target movie in 7-days opening, which is available in the Mojo Box office. In addition to the real-world benchmarking, we performed extensive experiments to demonstrate the accuracy and effectiveness of our proposed approach in comparison with the other state-of-the-art methods.

References:

[1]    Son, L.H., 2015. HU-FCF++. Engineering Applications of Artificial Intelligence, 41(C), pp.207-222.
[2]    Camacho, L.A.G. and Alves-Souza, S.N., 2018. Social network data to alleviate cold-start in recommender system: A systematic review. Information Processing & Management, 54(4), pp.529-544.
[3]    Otsuka, E., Wallace, S.A. and Chiu, D., 2016. A hashtag recommendation system for twitter data streams. Computational social networks, 3(1), p.3. 
[4]    Khan, F.H., Bashir, S. and Qamar, U., 2014. TOM: Twitter opinion mining framework using hybrid classification scheme. Decision support systems, 57, pp.245-257. 
[5]    Yang, X., Guo, Y., Liu, Y. and Steck, H., 2014. A survey of collaborative filtering based social recommender systems. Computer communications, 41, pp.1-10.
[6]    Anwaar, F., Iltaf, N., Afzal, H. and Nawaz, R., 2018. HRS-CE: A hybrid framework to integrate content embeddings in recommender systems for cold start items. Journal of computational science, 29, pp.9-18.
[7]    Illig, J., Hotho, A., Jäschke, R. and Stumme, G., 2007. A comparison of content-based tag recommendations in folksonomy systems. In Knowledge Processing and Data Analysis (pp. 136-149). Springer, Berlin, Heidelberg. 
[8]    Alahmadi, D.H. and Zeng, X.J., 2015, November. Twitter-based recommender system to address cold-start: A genetic algorithm based trust modelling and probabilistic sentiment analysis. In 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 1045-1052). IEEE. 
[9]    Dang, T.T., Duong, T.H. and Nguyen, H.S., 2014, December. A hybrid framework for enhancing correlation to solve cold-start problem in recommender systems. In the 2014 Seventh IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA) (pp. 1-5). IEEE.
[10]    Aharon, M., Anava, O., Avigdor-Elgrabli, N., Drachsler-Cohen, D., Golan, S. and Somekh, O., 2015, September. Excuseme: Asking users to help in item cold-start recommendations. In Proceedings of the 9th ACM Conference on Recommender Systems (pp. 83-90).
[11]    Choi, S.M., Ko, S.K. and Han, Y.S., 2012. A movie recommendation algorithm based on genre correlations. Expert Systems with Applications, 39(9), pp.8079-8085.
[12]    Santos, J., Peleja, F., Martins, F. and Magalhães, J., 2017, October. Improving cold-start recommendations with social-media trends and reputations. In International Symposium on Intelligent Data Analysis (pp. 297-309). Springer, Cham. 
[13]    Pandey, A.K. and Rajpoot, D.S., 2016, December. Resolving cold start problem in recommendation system using demographic approach. In 2016 International Conference on Signal Processing and Communication (ICSC) (pp. 213-218). IEEE.
[14]    Sun, D., Luo, Z. and Zhang, F., 2011, October. A novel approach for collaborative filtering to alleviate the new item cold-start problem. In 2011 11th International Symposium on Communications & Information Technologies (ISCIT) (pp. 402-406). IEEE.
[15]    Fernández-Tobías, I., Cantador, I., Tomeo, P., Anelli, V.W. and Di Noia, T., 2019. Addressing the user cold start with cross-domain collaborative filtering: exploiting item metadata in matrix factorization. User Modeling and User-Adapted Interaction, 29(2), pp.443-486.
[16]    Katarya, R., 2018. Movie recommender system with metaheuristic artificial bee. Neural Computing and Applications, 30(6), pp.1983-1990.
[17]    Guo, G., Zhang, J. and Yorke-Smith, N., 2015, February. Trustsvd: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In Twenty-Ninth AAAI Conference on Artificial Intelligence. 
[18]    Zhang, D., Hsu, C.H., Chen, M., Chen, Q., Xiong, N. and Lloret, J., 2013. Cold-start recommendation using bi-clustering and fusion for large-scale social recommender systems. IEEE Transactions on Emerging Topics in Computing, 2(2), pp.239-250.
[19]    Zhong, S., Zhang, W., Zhang, Q. and Lei, K., 2017, May. A trust networks recommender algorithm based on Latent Factor Model. In 2017 IEEE International Conference on Communications (ICC) (pp. 1-7). IEEE.
[20]    Thanh-Tai, H. and Thai-Nghe, N., 2017, November. A Semantic-Based Recommendation Approach for Cold-Start Problem. In International Conference on Future Data and Security Engineering (pp. 433-443). Springer, Cham.
[21]    Reshma, R., Ambikesh, G. and Thilagam, P.S., 2016, April. Alleviating data sparsity and cold start in recommender systems using social behaviour. In 2016 International Conference on Recent Trends in Information Technology (ICRTIT) (pp. 1-8). IEEE.
[22]    Revathy, V.R. and Pillai, A.S., 2019. A Proposed Architecture for Cold Start Recommender by Clustering Contextual Data and Social Network Data. In Computing, Communication and Signal Processing (pp. 323-331). Springer, Singapore.
[23]    Lee, M.R., Chen, T.T. and Cai, Y.S., 2016, August. Amalgamating social media data and movie recommendation. In Pacific Rim Knowledge Acquisition Workshop (pp. 141-152). Springer, Cham.
[24]    Rosli, A.N., You, T., Ha, I., Chung, K.Y. and Jo, G.S., 2015. Alleviating the cold-start problem by incorporating movies facebook pages. Cluster Computing, 18(1), pp.187-197.
[25]    Ji, K. and Shen, H., 2016. Jointly modeling content, social network and ratings for explainable and cold-start recommendation. Neurocomputing, 218, pp.1-12.
[26]    Natarajan, S. and Moh, M., 2016, October. Recommending news based on hybrid user profile, popularity, trends, and location. In 2016 international conference on collaboration technologies and systems (CTS) (pp. 204-211). IEEE.
[27]    Moshfeghi, Y., Piwowarski, B. and Jose, J.M., 2011, July. Handling data sparsity in collaborative filtering using emotion and semantic based features. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (pp. 625-634).
[28]    Ponnam, L.T., Punyasamudram, S.D., Nallagulla, S.N. and Yellamati, S., 2016, February. Movie recommender system using item based collaborative filtering technique. In 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS) (pp. 1-5). IEEE.
[29]    Deldjoo, Y., Dacrema, M.F., Constantin, M.G., Eghbal-Zadeh, H., Cereda, S., Schedl, M., Ionescu, B. and Cremonesi, P., 2019. Movie genome: alleviating new item cold start in movie recommendation. User Modeling and User-Adapted Interaction, 29(2), pp.291-343.
[30]    Yi, P., Yang, C., Zhou, X. and Li, C., 2016, September. A movie cold-start recommendation method optimized similarity measure. In 2016 16th International Symposium on Communications and Information Technologies (ISCIT) (pp. 231-234). IEEE.
[31]    Pirasteh, P., Jung, J.J. and Hwang, D., 2014, April. Item-based collaborative filtering with attribute correlation: a case study on movie recommendation. In Asian conference on intelligent information and database systems (pp. 245-252). Springer, Cham.
[32]    Baccianella, S., Esuli, A. and Sebastiani, F., 2010, May. Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In Lrec (Vol. 10, No. 2010, pp. 2200-2204).
[33]    http://github.com/word/emoji-emotion,last accessed on Dec, 2020.