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Chakraverty, Shampa
- Cross Domain Recommendation Using Vector Modeling and Genre Correlations
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Authors
Affiliations
1 Division of Computer Engineering, Netaji Subhas Institute of Technology, IN
1 Division of Computer Engineering, Netaji Subhas Institute of Technology, IN
Source
ICTACT Journal on Soft Computing, Vol 8, No 3 (2018), Pagination: 1649-1654Abstract
Recommender systems are basically information retrieval systems that offer guidance to users in making individual decisions related to choosing items based on personal interests. On Internet, there are infinite numbers of results for a particular query like movies, music, books, clothes etc. Sorting through every result is very tedious and time-consuming. Recommender system is very important application of data science and machine learning. They make the job of recommendation and prediction of preferences of users very simple. There are many limitations in classical recommender system because they provide recommendations in single domain only. With proliferating e-commerce sites and limitations in collaborative and content based recommender systems, cross domain recommender system are now widely in use. They can address the data sparsity and cold start problem by utilizing data from other related domains. In this paper, we propose recommendations across different domains by combining the benefit of plot keywords extracted from storyline and genre details from the two entertainment domains. We illustrate the working of our proposed CDR scheme using the movie as source domain and book as target domain.Keywords
Cross Domain Recommender System, Cold Start Problem, Keywords, Vector Modeling, Genre Correlation.
References
- B. Shapira, L. Rokach and S. Freilikhman, “Facebook Single and Cross Domain Data for Recommendation Systems”, User Modeling and User-Adapted Interaction, Vol. 23, No. 2-3, pp. 211-247, 2013.
- A. Majid, L. Chen and G. Chen, “A Context-Aware Personalized Travel Recommendation System based on Geo-Tagged Social Media Data Mining”, International Journal of Geographical Information Science, Vol. 27, No. 4, pp. 662-684, 2013.
- J.B. Schafer, D. Frankowski and J. Herlocker, “Collaborative Filtering Recommender Systems”, Springer, 2007.
- B. Li, Q. Yang and X. Xue, “Can Movies and Books Collaborate? Cross-Domain Collaborative Filtering for Sparsity Reduction”, Proceedings of International Joint Conference on Artificial Intelligence, pp. 2052-2057, 2009.
- G. Adomavicius and A. Tuzhilin, “Toward the Next Generation of Recommender Systems: A Survey of the State of-the-Art and Possible Extensions”, Introduction to Data Mining and Knowledge Discovery, Vol. 17, pp. 734-749, 2005.
- I. Cantador and P. Cremonesi, “Tutorial on Cross-Domain Recommender Systems”, Proceedings of 8th ACM Conference on Recommender Systems, pp. 401-402, 2014.
- S.M. Choi and Y.S. Han, “A Content Recommendation System based on Category Correlations”, Proceedings of 5th International Multi-Conference on Computing in the Global Information Technology, pp. 1257-1260, 2010.
- I. Fernandez-Tobias, I. Cantador, M. Kaminskas and F. Ricci, “A Generic Semantic-Based Framework for Cross-Domain Recommendation”, Proceedings of 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems, pp. 25-32, 2011.
- S. Berkovsky, T. Kuflik and F. Ricci, “Mediation of User Models for Enhanced Personalization in Recommender Systems”, User Modeling and User-Adapted Interaction, Vol. 18, No. 3, pp. 245-286, 2014.
- R. Lemdani, N. Bennacer, G. Polaillon and Y. Bourda, “A Collaborative and Semantic-based Approach for Recommender Systems”, Proceedings of 10th International Conference on Intelligent Systems Design and Applications, pp. 469-476, 2010.
- Karamollah Bagheri Fard, Mehrbakhsh Nilashi and Naomie Salim, “Recommender System based on Semantic Similarity”, International Journal of Electrical and Computer Engineering, Vol. 3, No. 6, pp. 751-759, 2013.
- Shampa Chakraverty and Mala Saraswat, “Review based Emotion Profiles for Cross Domain Recommendation”, Multimedia Tools and Applications, Vol. 76, No. 24, pp. 1-24, 2017.
- Yang Rui, Wei Hu and Yuzhong Qu, “Using Semantic Technology to Improve Recommender Systems Based on Slope One”, Proceedings of International Conference on Semantic Web, and Web Science, pp. 11-23, 2013.
- Magnus Sahlgren, “The Distributional Hypothesis”, Proceedings of International Conference on Linguistics, pp. 1-18, 2006.
- Kaggle Datasets, Available at: https://www.kaggle.com/datasets, Accessed on 2017.
- C.N. Ziegler et al., “Improving Recommendation Lists through Topic Diversification”, Proceedings of ACM 14th International Conference on World Wide Web, pp. 22-32, 2005.
- Sarcasm Detection in Online Review Text
Abstract Views :213 |
PDF Views:3
Authors
Affiliations
1 Division of Computer Engineering, Netaji Subhas Institute of Technology, IN
1 Division of Computer Engineering, Netaji Subhas Institute of Technology, IN
Source
ICTACT Journal on Soft Computing, Vol 8, No 3 (2018), Pagination: 1674-1679Abstract
Sarcasm is a type of sentiment where people express negative sentiment using positive connotation words in text and vice-versa. In this work, we propose a cross-domain sarcasm detection framework that allows acquisition, storage and processing of tweets for detecting sarcastic content in online reviews. We conduct our experiments on Amazon product review dataset namely the Sarcasm Corpus Version1 having about 2000 reviews. We use Support Vector Machines (SVM) and Neural Networks (NN) for detecting sarcasm using lexical, pragmatic, linguistic incongruity and context incongruity features. We report the results and present a comparative evaluation of SVM and NN classifiers for single domain sarcasm detection indicating their suitability for the task. Then, we use these models for cross-domain sarcasm detection. The experimental results indicate the reliability of our approach.Keywords
Sarcasm, Machine Learning, Support Vector Machines, Neural Network Classifier, Amazon, Twitter.References
- J.T. Hancock, “Verbal Irony use in Face-to-Face and Computer-Mediated Conversations”, Journal of Language and Social Psychology, Vol. 23, No. 4, pp. 447-463, 2004.
- I. Habernal and J. Hong, “Sarcasm Detection on Czech and English Twitter”, Proceedings of 25th International Conference on Computational Linguistics, pp. 121-127, 2014.
- Us Secret Service Seeks Twitter Sarcasm Detector, Available at: http://www.bbc.com/news/technology27711109, Accesssed on 2014.
- K. Nigam and M. Hurst, “Towards a Robust Metric of Polarity”, Proceedings of International Conference on Computing Attitude and Affect in Text: Theory and Applications, pp. 265-279, 2010.
- B. Pang and L. Lee, “Opinion Mining and Sentiment Analysis”, Now Publishers, 2008.
- Tony Veale and Yanfen Hao, “Detecting Ironic Intent in Creative Comparisons”, Proceedings of European Conference on Artificial Intelligence, pp. 765-770, 2010.
- S.K. Bharti, K.S. Babu and S.K. Jena, “Parsing-based Sarcasm Sentiment Recognition in Twitter Data”, Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1373-1380, 2015.
- C. Liebrecht, F. Kunneman and A. Van Den Bosch, “The Perfect Solution for Detecting Sarcasm in Tweets #Not”, Proceedings of 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 2937, 2013.
- P. Reyes and T. Veale, “A Multidimensional Approach for Detecting Irony in Twitter”, Proceedings of International Conference on Language Resources and Evaluation, pp. 130, 2012.
- R.J. Kreuz and G.M. Caucci, “Lexical Influences on the Perception of Sarcasm”, Proceedings of the Workshop on Computational Approaches to Figurative Language, pp. 14, 2007.
- E. Lunando and A. Purwarianti, “Indonesian Social Media Sentiment Analysis with Sarcasm Detection”, Proceedings of International Conference on Advanced Computer Science and Information Systems, pp. 212-215, 2013.
- Oren Tsur, Dmitry Davidov, and Ari Rappoport, “SemiSupervised Recognition of Sarcastic Sentences in Online Product Reviews”, Proceedings of 4th International Conference on Weblogs and Social Media, pp. 23-26, 2010.
- Roberto Gonzalez-Ibanez, Smaranda Muresan and Nina Wacholder, “Identifying Sarcasm in Twitter: a Closer Look”, Proceedings of 49th Annual Meeting of the Association for Computational Linguistics, pp. 581-586, 2011.
- E. Riloff, A. Qadir, P. Surve, L. De Silva, N. Gilbert and R. Huang, “Sarcasm as Contrast between a Positive Sentiment and Negative Situation”, Proceedings of Conference on Empirical Methods in Natural Language Processing, pp 704-714, 2013.
- Tomas Ptacek, Ivan Habernal and Jun Hong, “Sarcasm Detection on Czech and English Twitter”, Proceedings of 25th International Conference on Computational Linguistics, pp. 213-223, 2014.
- Senti-Strength Tool, Available at:http://sentistrength.wlv.ac.uk/
- Konstantin Buschmeier, Philipp Cimiano and Roman Klinger, “An Impact Analysis of Features in a Classification Approach to Irony Detection in Product Reviews”, Proceedings of 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 4249, 2014.
- D. Davidov, O. Tsur and A. Rappoport, “Semi-Supervised Recognition of Sarcastic Sentences in Twitter and Amazon”, Proceedings of 14th Conference on Computational Natural Language Learning, pp. 107-116, 2010.
- Mondher Bouazizi and Tomoaki Ohtsuki, “Sarcasm Detection in Twitter”, Proceedings of International Conference in Global Communications, pp. 331-334, 2015.
- Debanjan Ghosh, Weiwei Guo and Smaranda Muresan, “Sarcastic or Not: Word Embedding to Predict the Literal or Sarcastic Meaning of Words”, Proceedings of International Conference on Empirical Methods in Natural Language Processing, pp 1003-1012, 2015.
- Ashwin Rajadesingan, Reza Zafarani and Huan Liu, “Sarcasm Detection on Twitter: A Behavioral Modeling Approach”, Proceedings of 8th ACM International Conference on Web Search and Data Mining, pp. 97-101, 2015.
- Data Mining Project focused on Twitter Sarcasm Measurement, Available at: https://github.com/dmitryvinn/twitter-sarcasmmeasurement, Accessed on 2016.
- R.J. Kreuz and R.M. Roberts, “Two Cues for Verbal Irony: Hyperbole and the Ironic Tone of Voice”, Metaphor and Symbol, Vol. 10, No. 1, pp. 21-31, 1995.
- A Peer-Assessment Based Approach for Teaching Microprogramming
Abstract Views :237 |
PDF Views:105
Authors
Affiliations
1 Department of Computer Science and Engineering, Netaji Subhas University of Technology, New Delhi 110078, IN
1 Department of Computer Science and Engineering, Netaji Subhas University of Technology, New Delhi 110078, IN
Source
Journal of Engineering Education Transformations, Vol 34, No 4 (2021), Pagination: 7-12Abstract
The course on microprocessors introduces undergraduate computer science students to hardware-level programming. The course was taught by the authors to 130 students in context of the 8085 and 8086 microprocessors in the Spring semester of 2019. The students executed their programs on hardware kits, and participated in a double-blind peerassessment exercise in which they assessed and rated programs written by their peers and also advised them on improving the efficiency and readability of their programs. It was found that the peer-assessment exercise helped the students to learn better and score 6.97% higher marks in examination (Z=2.68, P&#;60;0.05). There was a moderately strong correlation (r=0.43, P&#;60;0.05) between the ratings received by the students from their peers and their score in the postintervention test. The students felt that they benefitted from the advice they received from their peers and 78% of the students agreed with the ratings they received. The students felt that assessing programs written by others also helped them in improving their own programming skills.Keywords
Computer Science Education, Microprocessor, Microprogramming, Peer-Assessment.Full Text