Prafulla Bafna
Shailaja Shirwaikar
ABSTRACT
Recommender Systems (RS) have increasingly evolved from novelties used by few E-commerce sites to an essential component of business tools handling the world of E-commerce. Recommender Systems have been widely used for product recommendations such as books and movies as well as, it is also gaining ground in service recommendations such as hotels, restaurants and travel attractions. Collaborative filtering based on reviews and ratings is usually applied that uses Clustering technique. The primary step of converting textual reviews into a Feature Matrix (FM) can be greatly refined by using semantic similarity between terms. In this paper Wordnet based Synset grouping approach is presented that not only reduces dimensions in FM but also generates Feature vectors (FV) for each cluster with significantly improved cluster quality. The paper presents a three step approach of validating the reviews, grouping of reviews and review based recommendations using Feature vector. Real datasets extracted from travel sites are used for experiments.
- Bafna, P.B., Shirwaikar, S. and Pramod, D., 2016. Multi-Step Iterative Algorithm for Feature Selection on Dynamic Documents. International Journal of Information Retrieval Research (IJIRR), 6(2), pp. 24--40 Google Scholar
Digital Library
- Beil, F., Ester, M. and Xu, X., 2002, July. Frequent term-based text clustering. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 436--442). ACM. Google ScholarDigital Library
- Castagnos, S. and Boyer, A., 2007, July. Modeling preferences in a distributed recommender system. In International Conference on User Modeling (pp. 400--404). Springer Berlin Heidelberg. Google ScholarDigital Library
- Debnath, S., Ganguly, N. and Mitra, P., 2008, April. Feature weighting in content based recommendation system using social network analysis. In Proceedings of the 17th international conference on World Wide Web (pp. 1041--1042). ACM. Google ScholarDigital Library
- Faridani, S., 2011, October. Using canonical correlation analysis for generalized sentiment analysis, product recommendation and search. In Proceedings of the fifth ACM conference on Recommender systems (pp. 355--358). ACM. Google ScholarDigital Library
- Glance, N., Meunier, J.L., Bernard, P. and Arregui, D., 2001, September. Collaborative document monitoring. In Proceedings of the 2001 International ACM SIGGROUP Conference on Supporting Group Work (pp. 171--178). ACM. Google ScholarDigital Library
- Goldberg, D., Nichols, D., Oki, B.M. and Terry, D., 1992. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), pp. 61--70. Google ScholarDigital Library
- Halkidi, M., Batistakis, Y. and Vazirgiannis, M., 2002. Cluster validity methods: part I. ACM Sigmod Record, 31(2), pp. 40--45. Google ScholarDigital Library
- Herlocker, Jonathan L., Joseph A. Konstan, Loren G. Terveen, and John T. Riedl. "Evaluating collaborative filtering recommender systems." ACM Transactions on Information Systems (TOIS) 22, no. 1 (2004): 5--53. Google ScholarDigital Library
- Hung, L.V., Anh, N.T.K. and Dang, N.H., 2012, August. Improving Vietnamese web page clustering by combining neighbors' content and using iterative feature selection. In Proceedings of the Third Symposium on Information and Communication Technology (pp. 47--54). ACM. Google ScholarDigital Library
- Jannach, D., Zanker, M., Felfernig, A. and Friedrich, G., 2010. Recommender systems: an introduction. Cambridge University Press. Google ScholarDigital Library
- Janssens, F., Glänzel, W. and De Moor, B., 2007, August. Dynamic hybrid clustering of bioinformatics by incorporating text mining and citation analysis. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 360--369). ACM. Google ScholarDigital Library
- Jashki, A., Makki, M., Bagheri, E. and Ghorbani, A.A., 2009. An iterative hybrid filter-wrapper approach to feature selection for document clustering. In Proceedings of the 22nd Canadian Conference on Artificial Intelligence (AI'09) 2009. Google ScholarDigital Library
- Jiawei Han, M.K. and Pei, J., 2011. Data Mining: Concepts and Techniques: Concepts and Techniques. Google ScholarDigital Library
- Jayabharathy, J. and Kanmani, S., 2014. Correlated concept based dynamic document clustering algorithms for newsgroups and scientific literature. Decision Analytics, 1(1), p.1.Google ScholarCross Ref
- Karypis, G., 2001, October. Evaluation of item-based top-n recommendation algorithms. In Proceedings of the tenth international conference on Information and knowledge management (pp. 247--254). ACM. Google ScholarDigital Library
- Kumar, A., Kumar, N., Hussain, M., Chaudhury, S. and Agarwal, S., 2014, December. Semantic clustering-based cross-domain recommendation. In Computational Intelligence and Data Mining (CIDM), 2014 IEEE Symposium on (pp. 137--141). IEEE.Google Scholar
- Li, Q. and Kim, B.M., 2003, October. Clustering approach for hybrid recommender system. In Web Intelligence, 2003. WI 2003. Proceedings. IEEE/WIC International Conference on (pp. 33--38). IEEE. Google ScholarDigital Library
- Li, T., Zhang, Y. and Sindhwani, V., 2009, August. A nonnegative matrix tri-factorization approach to sentiment classification with lexical prior knowledge. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1-Volume 1 (pp. 244--252). Association for Computational Linguistics. Google ScholarDigital Library
- Lops, P., De Gemmis, M. and Semeraro, G., 2011. Content-based recommender systems: State of the art and trends. In Recommender systems handbook (pp. 73--105). Springer US.Google Scholar
- Melville, P., Gryc, W. and Lawrence, R.D., 2009, June. Sentiment analysis of blogs by combining lexical knowledge with text classification. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1275--1284). ACM. Google ScholarDigital Library
- Miller, B.N., Albert, I., Lam, S.K., Konstan, J.A. and Riedl, J., 2003, January. MovieLens unplugged: experiences with an occasionally connected recommender system. In Proceedings of the 8th international conference on Intelligent user interfaces (pp. 263--266). ACM. Google ScholarDigital Library
- Pantel, P. and Lin, D., 2002, August. Document clustering with committees. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 199--206). ACM. Google ScholarDigital Library
- Peis, E., del Castillo, J.M. and Delgado-López, J.A., 2008. Semantic recommender systems. analysis of the state of the topic. Hipertext.net, 6, pp. 1--5.Google Scholar
- Resnick, P. and Varian, H.R., 1997. Recommender systems. Communications of the ACM, 40(3), pp. 56--58. Google ScholarDigital Library
- Richardson, R., Smeaton, A. and Murphy, J., 1994. Using WordNet as a knowledge base for measuring semantic similariy between words.Google Scholar
- Rafi, M. and Shaikh, M.S., 2013. An improved semantic similarity measure for document clustering based on topic maps. arXiv preprint arXiv:1303.4087.Google Scholar
- Roshchina, A., Cardiff, J. and Rosso, P., 2012. Evaluating the Similarity Estimator Component of the TWIN Personality-based Recommender System.Google Scholar
- Sarwar, B., Karypis, G., Konstan, J. and Riedl, J., 2001, April. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web (pp. 285--295). ACM. Google ScholarDigital Library
- Sarwar, B., Karypis, G., Konstan, J. and Riedl, J., 2000, October. Analysis of recommendation algorithms for ecommerce. In Proceedings of the 2nd ACM conference on Electronic commerce (pp. 158--167). ACM. Google ScholarDigital Library
- Sarwar, B., Karypis, G., Konstan, J. and Riedl, J., 2000. Application of dimensionality reduction in recommender system-a case study (No. TR-00-043). Minnesota Univ Minneapolis Dept of Computer Science.Google Scholar
- Sarwar, B.M., Konstan, J.A., Borchers, A., Herlocker, J., Miller, B. and Riedl, J., 1998, November. Using filtering agents to improve prediction quality in the grouplens research collaborative filtering system. In Proceedings of the 1998 ACM conference on Computer supported cooperative work (pp. 345--354). ACM. Google ScholarDigital Library
- Schafer, J.B., Konstan, J. and Riedl, J., 1999, November. Recommender systems in e-commerce. In Proceedings of the 1st ACM conference on Electronic commerce (pp. 158--166). ACM. Google ScholarDigital Library
- Webster, A. and Vassileva, J., 2007, July. Push-poll recommender system: Supporting word of mouth. In International Conference on User Modeling (pp. 278--287). Springer Berlin Heidelberg. Google ScholarDigital Library
- Wei, T., Lu, Y., Chang, H., Zhou, Q. and Bao, X., 2015. A semantic approach for text clustering using WordNet and lexical chains. Expert Systems with Applications, 42(4), pp. 2264--2275. Google ScholarDigital Library
Index Terms
- Semantic Clustering Driven Approaches to Recommender Systems
Recommendations
Improving Accuracy of Recommender System by Item Clustering
Recommender System (RS) predicts user's ratings towards items, and then recommends highly-predicted items to user. In recent years, RS has been playing more and more important role in the agent research field. There have been a great deal of researches ...
A New Approach for Recommender System
ICACS '17: Proceedings of the 1st International Conference on Algorithms, Computing and SystemsIn today's e-commerce environment, Collaborative Filtering (CF) is a widely used algorithm for recommender system, which is to identify the users who have similar preferences to the target user, and to predict the preference of the target user according ...
Incorporating user rating credibility in recommender systems
AbstractThere have been many research efforts aimed at improving recommendation accuracy with Collaborative Filtering (CF). Yet there is still a lack of investigation into the integration of CF algorithms with the analysis of users’ rating behaviors. In ...
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