Distributed Recommender Systems with Sentiment Analysis

Öz

In this research rating based recommender system (RS) on sentiment analysis (SA) by using online reviews data by store big data technology. Online reviews are important to understand users decide to buy a product, see a movie or buy a food user feedback. However nowadays collect lots of reviews from user feedback on e-commerce web sites therefore the importance of increasing big data technology, at the same time increasing needs of big calculation. We report on our classification effort on the sentiment information of reviews, structure of distributed file system and frameworks. Our work focuses on information from reviews to improving recommendation accuracy with the big data era.

Anahtar Kelimeler

• recommender systems
• big data
• distributed systems
• sentiment analysis

Büyük Veride Tavsiye Sistemlerini Duygu Analizi ile Desteklemek

Öz

Bu çalışmanın amacı kullanıcı puanlama temelli tavsiye sistemlerinin, kullanıcı puanları yerine duygu analizinden elde edilen değerler ile büyük veri üzerinden gerçeklenmesidir. Internet üzerinden e-ticaret sistemlerinin yaygınlaşması ile çok fazla kullanıcı verisi oluşması, alışılmış depolama sistemlerinin artık yeterli gelmemesi ve verinin bölünmesi durumunu oluşturmuştur. Ancak dağıtık dosya sistemleri teknolojileri ile veri bütünlüğünü sağlamak mümkündür. Bu veri üzerinden makine öğrenmesi algoritmalarının çalıştırılması ve sonuçların değerlendirilmesine büyük ihtiyaç duyulmaktadır. Bu çalışmada tavsiye sistemlerinin dağıtık veri üzerinden değerlendirilmesinde, doğal dil işleme adımlarının sisteme eklenmesi ile sağlanan iyileştirme raporlanmıştır.

Anahtar Kelimeler

• tavsiye sistemleri
• büyük veri
• dağıtık dosya sistemleri
• duygu analizi

Kaynakça

1. Bell, R. M., & Koren, Y. (2007, October). Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In Data Mining, 2007. ICDM 2007. Seventh IEEE International Conference on (pp. 43-52). IEEE, ISO 690.
2. Zhou, B., Jia, Y., Liu, C., & Zhang, X. (2010, October). A distributed text mining system for online web textual data analysis. In Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2010 International Conference on (pp. 1-4). IEEE.
3. S Meng, S., Dou, W., Zhang, X., & Chen, J. (2014). Kasr: A keyword-aware service recommendation method on mapreduce for big data applications. Parallel and Distributed Systems, IEEE Transactions on, 25(12), 3221-3231.
4. Yang, X., Guo, Y., & Liu, Y. (2013). Bayesian-inference-based recommendation in online social networks. Parallel and Distributed Systems, IEEE Transactions on, 24(4), 642-651.
5. Kang, G., Liu, J., Tang, M., Liu, X., Cao, B., & Xu, Y. (2012, June). AWSR: Active web service recommendation based on usage history. In Web Services (ICWS), 2012 IEEE 19th International Conference on (pp. 186-193). IEEE.
6. Chen, Y. Y., Cheng, A. J., & Hsu, W. H. (2013). Travel recommendation by mining people attributes and travel group types from community-contributed photos. Multimedia, IEEE Transactions on, 15(6), 1283-1295.
7. Sanchez, F., Alduán, M., Alvarez, F., Menéndez, J. M., & Baez, O. (2012). Recommender system for sport videos based on user audiovisual consumption. Multimedia, IEEE Transactions on, 14(6), 1546-1557.
8. Zheng, Z., Wu, X., Zhang, Y., Lyu, M. R., & Wang, J. (2013). QoS ranking prediction for cloud services. Parallel and Distributed Systems, IEEE Transactions on, 24(6), 1213-1222.

9. White, T. (2012). Hadoop: The definitive guide. " O'Reilly Media, Inc.".
10. Anil, R., Dunning, T., & Friedman, E. (2011). Mahout in action (pp. 145-183). Shelter Island: Manning.
11. Wilson, T.; Wiebe, J.; and Hoffmann, P., (2009). Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis. Computational Linguistics 35(3):399–433.
12. Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bethard, S., & McClosky, D. (2014, June). The Stanford CoreNLP Natural Language Processing Toolkit. In ACL (System Demonstrations) (pp. 55-60).
13. Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bethard, S., & McClosky, D. (2014, June). The Stanford CoreNLP Natural Language Processing Toolkit. In ACL (System Demonstrations) (pp. 55-60).
14. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational linguistics, 37(2), 267-307.
15. Prem Melville and Vikas Sindhwani, “Recommender Systems”, IBM T.J. Watson Research Center.
16. Owen S., Anil R., Dunning T. and Friedman E.: “Mahout In Action”, (2012). Manning Publications Co. ISBN 978-1- 9351-8268-9
17. McAuley, J., Targett, C., Shi, Q., & van den Hengel, A. (2015, August). Image-based recommendations on styles and substitutes. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 43-52). ACM.
18. McAuley, J., Pandey, R., & Leskovec, J. (2015, August). Inferring networks of substitutable and complementary products. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(pp. 785-794). ACM.
19. McAuley, J. J., & Leskovec, J. (2013, May). From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. In Proceedings of the 22nd international conference on World Wide Web (pp. 897-908). International World Wide Web Conferences Steering Committee.
20. McAuley, J. J., & Leskovec, J. (2013, May). From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. In Proceedings of the 22nd international conference on World Wide Web (pp. 897-908). International World Wide Web Conferences Steering Committee.
21. Xing Margaret, F. U., & Xiaocheng, L. I. (2015). From Movie Reviews to Restaurants Recommendation.