@inproceedings{cede9d9f8b88461a92ef62ed9e79665b,
title = "Embedding User Behavioral Aspect in TF-IDF Like Representation",
abstract = "Term Frequency - Inverse Document Frequency (TF-IDF) computes weight for each word in a document which increases proportionally to the number of times the word appears in a specific document but is counterbalanced by the number of times it occurs in the collection of documents. TF-IDF is the state-of-the-art for computing relevancy scores between documents. However, it is based on statistical learning alone and doesn't directly capture the conceptual contents of the text or the behavioral aspects of the writer. Hence, in this work we show how relatively low dimensional user behavioral vectors extracted from the same text, from which TF-IDF vectors are extracted, can be used to enrich the performance of TF-IDF. We extract User-Concerns embedded in user reviews and append them to TF-IDF vectors to train a deep rating prediction model. Our experiments show that adding such conceptual knowledge to TF-IDF vectors can significantly enhance the performance of TF-IDF vectors by only adding very little complexity.",
keywords = "TF-IDF, rating prediction, topic modeling, user behavior, user concerns",
author = "Ligaj Pradhan and Chengcui Zhang and Steven Bethard and Xin Chen",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 1st IEEE Conference on Multimedia Information Processing and Retrieval, MIPR 2018 ; Conference date: 10-04-2018 Through 12-04-2018",
year = "2018",
month = jun,
day = "26",
doi = "10.1109/MIPR.2018.00061",
language = "English (US)",
series = "Proceedings - IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "262--267",
booktitle = "Proceedings - IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR 2018",
}