Abstract
Most item shopping websites currently provide social network services (SNS) to collect their users’ opinions on items available for purchasing. This information is often used to reduce information overload and improve both the efficiency of the marketing process and user’s experience by means of user-modeling and hyper-personalization of contents. Whereas a variety of recommendation systems focus almost exclusively on ranking the items, we intend to extend this basic approach by predicting the sets of words that users would use should they express their opinions and interests on items not yet reviewed. To this end, we pay careful attention to the internal consistency of our model by relying on well-known facts of linguistic analysis, collaborative filtering techniques and matrix factorization methods. Still at an early stage of development, we discuss some encouraging results and open challenges of this new approach.
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Notes
- 1.
From now on, we will use the opinion and review terms without distinction whenever we refer to the text (or a representative part of it) that a user writes about a product.
- 2.
We work with opinion-words provided by a natural language analysis tool. Terms may be compositions of several words.
- 3.
- 4.
- 5.
We use 20 executors with 8 cores and 16 GB RAM on a Hadoop cluster with a total of 695 GB RAM, 336 cores, and 2 TB HDFS. Our implemented algorithms are easily scalable, so any RAM limitation might be solved using a cluster with a sufficiently large number of nodes.
- 6.
It is part of the MLlib Apache’s Library.
- 7.
Apache Spark’s scalable machine learning library.
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Acknowledgements
The authors want to thank Bitext (http://bitext.com) for providing NLP services for research. They also acknowledge the support of the Universidad Europea de Madrid through the E-Modelo research project. Special thanks to Hugo Ferrando for developing a significant part of the code used for experimentation, and to Javier García-Blas for their insightful comments. This work has been partially supported by the Spanish7 Ministry of Economy and Competitiveness through the MTM2014-57158-R project.
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García-Cuesta, E., Gómez-Vergel, D., Gracia-Expósito, L., López-López, J.M., Vela-Pérez, M. (2018). Prediction of User Interest by Predicting Product Text Reviews. In: De Marsico, M., di Baja, G., Fred, A. (eds) Pattern Recognition Applications and Methods. ICPRAM 2017. Lecture Notes in Computer Science(), vol 10857. Springer, Cham. https://doi.org/10.1007/978-3-319-93647-5_8
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