Abstract
The main purpose of web search engines is to provide the user with most relevant results based on the searched keywords. Finding content that meets the expectations of the user and the relevance of the content to these needs are very important for the success of the search engine. In this study; with the keywords written to the search engine, it is aimed to reach not only the results which contains these terms, but also the semantically related results. Words and phrases in all documents to be searched will be vectorized with Word2Vec model, then phrases will be clustered based on their similarity values. Finally, these outputs will be integrated into a Lucene based NoSQL solution at index time. The study will be used for Kariyer.net’s job search engine. This study includes research and applications in word embeddings, machine learning, unsupervised learning.
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References
DiceTechJobs/ConceptualSearch (2015). https://github.com/DiceTechJobs/ConceptualSearch. Accessed 21 Jan 2020
Kim, W., Kim, D., Jang, H.: Semantic Extention Search for Documents Using the Word2vec (2016)
Gauch, S., Madrid, J.M., Induri, S., Ravindran, D., Chadalavada, S.: KeyConcept: A Conceptual Search Engine (2004)
Ravindran, D., Gauch, S.: Exploiting hierarchical relationships in conceptual search (2004)
Stokoe, C., Oakes, M., Tait, J.: Word Sense Disambiguation in Information Retrieval Revisited (2003)
Roul, R.K., Devanand, O.R., Sahay, S.K.: Web Document Clustering and Ranking using Tf-Idf based Apriori Approach (2014)
gensim 3.8.1 (2019). https://pypi.org/project/gensim. Accessed 11 Apr 2020
Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of Tricks for Efficient Text Classification (2016)
FastText (2020). https://fasttext.cc, Accessed 11 Apr 2020
Elankavi, R., Kalaiprasath, R., Udayakumar, R.: A fast clustering algorithm for high-dimensional data (2017)
Ulrike von Luxburg: A Tutorial on Spectral Clustering (2007)
Campello, R.J.G.B., Moulavi, D., Sander, J.: Density-based clustering based on hierarchical density estimates (2013)
İnsanların Kariyerlerindeki Bir Sonraki Pozisyonun Tahmin Edilmesi – 2 (2018). https://arge.kariyer.net/Makale/Ust-ve-Esdeger-Pozisyonlarin-Bulunmasi. Accessed 17 Apr 2020
What is Elasticsearch (2020). https://www.elastic.co/what-is/elasticsearch. Accessed 07 May 2020
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Tuncer, I., Kara, K.C., Karakaş, A. (2021). Improving Search Relevance with Word Embedding Based Clusters. In: Hemanth, J., Yigit, T., Patrut, B., Angelopoulou, A. (eds) Trends in Data Engineering Methods for Intelligent Systems. ICAIAME 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-030-79357-9_3
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DOI: https://doi.org/10.1007/978-3-030-79357-9_3
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