Abstract
Recommender systems are firmly established as a standard technology for assisting users with their choices. There are a variety of recommendation systems that exist which recommend products to users; however, they all focus on finding what the user likes to recommend them products. There has been very little, if at all, research done that focuses on what products the users should be avoiding. In this article, we propose a framework that focuses on computing a list of products that a user should be avoiding. We will be using explicit feedback provided by the user, in the form of reviews. From the reviews posted by a user, we hope to understand attributes about products that they do not like to build a user profile. To achieve that, Stanford CoreNLP 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). tool will be used to find out the keywords and the sentiment values for those keywords. We will also find keywords users used to describe a product to build the profile for the product. After creating our model, we test our framework by computing a list of products that a user should be avoiding. Based on our result, a confusion matrix is created to test the accuracy of our framework. Finally, we formulate challenges and improvement for future research on our approach to recommender system.
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References
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Dhaliwal, M., Rokne, J., Alhajj, R. (2018). Recommender System for Product Avoidance. In: Moshirpour, M., Far, B., Alhajj, R. (eds) Applications of Data Management and Analysis . Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-95810-1_9
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