MovieTweetings
Creators:
Dooms, Simon; De Pessemier, Toon; Martens, Luc
Publication Date:
2013
Data Category:
Dataset Description:
Publications Citing This Dataset:
Dooms, S., De Pessemier, T., & Martens, L. (2013). MovieTweetings: a movie rating dataset collected from twitter. Workshop on Crowdsourcing and Human Computation for Recommender Systems, Held in Conjunction with the 7th ACM Conference on Recommender Systems. Presented at the Workshop on Crowdsourcing and Human Computation for Recommender Systems (CrowdRec 2013), held in conjunction with the 7th ACM Conference on Recommender Systems (RecSys 2013), Hong Kong, China.
https://www.semanticscholar.org/paper/MovieTweetings-a-movie-rating-dataset-collected-Dooms-Pessemier/a89f70b203a4c6afac397b605aeb76efbdbd0b2a?sort=relevance&page=2
Dooms, S., Pessemier, T.D., & Martens, L. (2013). MovieTweetings: a movie rating dataset collected from twitter. ACM Conference on Recommender Systems.
DOI:10.1145/2559858.2559862
Dooms, S., Pessemier, T.D., & Martens, L. (2014). Mining cross-domain rating datasets from structured data on twitter. Proceedings of the 23rd International Conference on World Wide Web.
DOI:10.1145/2567948.2579232
Santos, J. (2014). Social-media monitoring for cold-start recommendations.
https://run.unl.pt/bitstream/10362/14826/1/Santos_2014.pdf
Pichl, M., Zangerle, E., & Specht, G. (2014). Combining Spotify and Twitter Data for Generating a Recent and Public Dataset for Music Recommendation. Grundlagen von Datenbanken.
https://ceur-ws.org/Vol-1313/paper_7.pdf
Kuchar, J. (2015). Augmenting a Feature Set of Movies Using Linked Open Data. Challenge+DC@RuleML.
https://ceur-ws.org/Vol-1417/paper16.pdf
Diaz-Aviles, E., Lam, H.T., Pinelli, F., Braghin, S., Gkoufas, Y., Berlingerio, M., & Calabrese, F. (2014). Predicting User Engagement in Twitter with Collaborative Ranking. ArXiv, abs/1412.7990.
DOI:10.1145/2668067.2668072
Abdollahi, B., Badami, M., Nutakki, G.C., Sun, W., & Nasraoui, O. (2014). A Two Step Ranking Solution for Twitter User Engagement. Proceedings of the 2014 Recommender Systems Challenge on - RecSysChallenge '14.
DOI:10.1145/2668067.2668081
Gao, M., & Zhang, X. (2015). A Movie Recommender System from Tweets Data.
http://cs229.stanford.edu/proj2015/299_poster.pdf
Magalhães, J.J., Pessoa, R., Souza, C.C., Costa, E., & Fechine, J.M. (2014). A Recommender System for Predicting User Engagement in Twitter. RecSysChallenge '14.
DOI:10.1145/2668067.2668078
Theuerkauf, R., Seyffahrt, T., & Peters, R. (2021). Improving Recommender Systems by Using Time-Weighted Sentiment Analysis. Proceedings of the 5th International Conference on E-Commerce, E-Business and E-Government.
DOI:10.1145/3466029.3466057
Çano, E., & Morisio, M. (2015). Characterization of public datasets for Recommender Systems. 2015 IEEE 1st International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), 249-257.
DOI:10.1109/RTSI.2015.7325106
Houari, N.S., Kabli, F., & Benyakoub, S. (2022). Toward a movie recommender system based on association rules and LDA approach. 2022 First International Conference on Big Data, IoT, Web Intelligence and Applications (BIWA), 25-30.
DOI:10.1109/BIWA57631.2022.10037941
Sokol, A. (2019). Movie Recommender System. Southeast Europe Journal of Soft Computing.
DOI:10.21533/scjournal.v8i2.180
Tahmasebi, H., Ravanmehr, R., & Mohamadrezaei, R. (2020). Social movie recommender system based on deep autoencoder network using Twitter data. Neural Computing and Applications, 1-17.
DOI:10.1007/s00521-020-05085-1
Sun, L. (2018). Predict Movie Revenue With Movie Meta-Data.
https://www.semanticscholar.org/paper/Predict-Movie-Revenue-With-Movie-Meta-Data-Sun/a603e85e2e743f6e3c92530bea000ff1a4cb0fd0
Li, W., Dong, Q., & Fu, Y. (2017). Investigating the Temporal Effect of User Preferences with Application in Movie Recommendation. Mob. Inf. Syst.
2017, 8940709:1-8940709:10
Zamani, H., Shakery, A., & Moradi, P. (2014). Regression and Learning to Rank Aggregation for User Engagement Evaluation. ArXiv, abs/1501.07467.
DOI:10.1145/2668067.2668077
Pritsker, E.W., Kuflik, T., & Minkov, E. (2017). Assessing the Contribution of Twitter's Textual Information to Graph-based Recommendation. Proceedings of the 22nd International Conference on Intelligent User Interfaces.
DOI:10.1145/3025171.3025218
Khan, E.M., Mukta, M.S., Ali, M.E., & Mahmud, J. (2020). Predicting Users’ Movie Preference and Rating Behavior from Personality and Values. ACM Transactions on Interactive Intelligent Systems (TiiS), 10, 1 - 25.
DOI:10.1145/3338244
Tsaku, N.Z., & Kosaraju, S.C. (2019). Boosting Recommendation Systems through an Offline Machine Learning Evaluation Approach. Proceedings of the 2019 ACM Southeast Conference.
DOI:10.1145/3299815.3314454
Ruan, Y., & Lin, T. (2016). An Integrated Recommender Algorithm for Rating Prediction.
https://arxiv.org/pdf/1608.02021.pdf
Ghanmode, I., & Tintarev, N. (2018). MovieTweeters: An Interactive Interface to Improve Recommendation Novelty. IntRS@RecSys.
https://ceur-ws.org/Vol-2225/paper4.pdf
https://www.semanticscholar.org/paper/MovieTweetings-a-movie-rating-dataset-collected-Dooms-Pessemier/a89f70b203a4c6afac397b605aeb76efbdbd0b2a?sort=relevance&page=2
Dooms, S., Pessemier, T.D., & Martens, L. (2013). MovieTweetings: a movie rating dataset collected from twitter. ACM Conference on Recommender Systems.
DOI:10.1145/2559858.2559862
Dooms, S., Pessemier, T.D., & Martens, L. (2014). Mining cross-domain rating datasets from structured data on twitter. Proceedings of the 23rd International Conference on World Wide Web.
DOI:10.1145/2567948.2579232
Santos, J. (2014). Social-media monitoring for cold-start recommendations.
https://run.unl.pt/bitstream/10362/14826/1/Santos_2014.pdf
Pichl, M., Zangerle, E., & Specht, G. (2014). Combining Spotify and Twitter Data for Generating a Recent and Public Dataset for Music Recommendation. Grundlagen von Datenbanken.
https://ceur-ws.org/Vol-1313/paper_7.pdf
Kuchar, J. (2015). Augmenting a Feature Set of Movies Using Linked Open Data. Challenge+DC@RuleML.
https://ceur-ws.org/Vol-1417/paper16.pdf
Diaz-Aviles, E., Lam, H.T., Pinelli, F., Braghin, S., Gkoufas, Y., Berlingerio, M., & Calabrese, F. (2014). Predicting User Engagement in Twitter with Collaborative Ranking. ArXiv, abs/1412.7990.
DOI:10.1145/2668067.2668072
Abdollahi, B., Badami, M., Nutakki, G.C., Sun, W., & Nasraoui, O. (2014). A Two Step Ranking Solution for Twitter User Engagement. Proceedings of the 2014 Recommender Systems Challenge on - RecSysChallenge '14.
DOI:10.1145/2668067.2668081
Gao, M., & Zhang, X. (2015). A Movie Recommender System from Tweets Data.
http://cs229.stanford.edu/proj2015/299_poster.pdf
Magalhães, J.J., Pessoa, R., Souza, C.C., Costa, E., & Fechine, J.M. (2014). A Recommender System for Predicting User Engagement in Twitter. RecSysChallenge '14.
DOI:10.1145/2668067.2668078
Theuerkauf, R., Seyffahrt, T., & Peters, R. (2021). Improving Recommender Systems by Using Time-Weighted Sentiment Analysis. Proceedings of the 5th International Conference on E-Commerce, E-Business and E-Government.
DOI:10.1145/3466029.3466057
Çano, E., & Morisio, M. (2015). Characterization of public datasets for Recommender Systems. 2015 IEEE 1st International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), 249-257.
DOI:10.1109/RTSI.2015.7325106
Houari, N.S., Kabli, F., & Benyakoub, S. (2022). Toward a movie recommender system based on association rules and LDA approach. 2022 First International Conference on Big Data, IoT, Web Intelligence and Applications (BIWA), 25-30.
DOI:10.1109/BIWA57631.2022.10037941
Sokol, A. (2019). Movie Recommender System. Southeast Europe Journal of Soft Computing.
DOI:10.21533/scjournal.v8i2.180
Tahmasebi, H., Ravanmehr, R., & Mohamadrezaei, R. (2020). Social movie recommender system based on deep autoencoder network using Twitter data. Neural Computing and Applications, 1-17.
DOI:10.1007/s00521-020-05085-1
Sun, L. (2018). Predict Movie Revenue With Movie Meta-Data.
https://www.semanticscholar.org/paper/Predict-Movie-Revenue-With-Movie-Meta-Data-Sun/a603e85e2e743f6e3c92530bea000ff1a4cb0fd0
Li, W., Dong, Q., & Fu, Y. (2017). Investigating the Temporal Effect of User Preferences with Application in Movie Recommendation. Mob. Inf. Syst.
2017, 8940709:1-8940709:10
Zamani, H., Shakery, A., & Moradi, P. (2014). Regression and Learning to Rank Aggregation for User Engagement Evaluation. ArXiv, abs/1501.07467.
DOI:10.1145/2668067.2668077
Pritsker, E.W., Kuflik, T., & Minkov, E. (2017). Assessing the Contribution of Twitter's Textual Information to Graph-based Recommendation. Proceedings of the 22nd International Conference on Intelligent User Interfaces.
DOI:10.1145/3025171.3025218
Khan, E.M., Mukta, M.S., Ali, M.E., & Mahmud, J. (2020). Predicting Users’ Movie Preference and Rating Behavior from Personality and Values. ACM Transactions on Interactive Intelligent Systems (TiiS), 10, 1 - 25.
DOI:10.1145/3338244
Tsaku, N.Z., & Kosaraju, S.C. (2019). Boosting Recommendation Systems through an Offline Machine Learning Evaluation Approach. Proceedings of the 2019 ACM Southeast Conference.
DOI:10.1145/3299815.3314454
Ruan, Y., & Lin, T. (2016). An Integrated Recommender Algorithm for Rating Prediction.
https://arxiv.org/pdf/1608.02021.pdf
Ghanmode, I., & Tintarev, N. (2018). MovieTweeters: An Interactive Interface to Improve Recommendation Novelty. IntRS@RecSys.
https://ceur-ws.org/Vol-2225/paper4.pdf
Variables:
Name | Description |
---|---|
userid | userid |
twitter_id | twitter_id |
movie_id | movie_id |
movie_title (movie_year) | movie_title (movie_year), e.g., Pulp Fiction (1994) |
genre|genre|genre | genre, e.g., Crime|Thriller |
rating | rating, e.g., 9 |
rating_timestamp | rating_timestamp, e.g., 1375657563 |
Details:
Publisher:
Workshop on Crowdsourcing and Human Computation for Recommender Systems, held in conjunction with the 7th ACM Conference on Recommender Systems
Dataset Size:
26.2 MB on GitHub (only ratings, maybe compressed)
Formats:
CSV
License:
136
Publication Date:
2013