The k Closest Resemblance Classifier for Amazon Products Recommender System

Nabil Belacel, Guangze Wei, Yassine Bouslimani

Abstract

This paper presents the application of classification method based on outranking approach to Content Based Filtering (CBF) recommendation system. CBF intends to recommend items similar to those a given user would have liked in the past by first extracting traditional content features such as keywords and then predicts user preferences. Therefore content based filtering system recommends an item to a user based upon a description of the item and a profile of the user’s interests. Typically, to represent user’s and items’ profiles the existing CBF recommendation systems use the vector space model with basic term frequency and inverse document frequency (tfidf ) weighting. The tfidf and cosine similarity techniques are able, in some cases, to obtain good performances, however, they do not handle imprecision of features’ scores and they allow the compensation between features which will lead to bad results. This paper introduces k Closest resemblance classifier for CBF. The detailed models in this paper were tested and compared with the well-known tfidf based the k Nearest Neighbor classifier using Amazon fine food and book reviews data-set. The preliminary results show that our proposed model can substantially improve personalized recommendation of items described with short text like products description and customers’ review.

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Paper Citation


in Harvard Style

Belacel N., Wei G. and Bouslimani Y. (2020). The k Closest Resemblance Classifier for Amazon Products Recommender System.In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-395-7, pages 873-880. DOI: 10.5220/0009155108730880


in Bibtex Style

@conference{icaart20,
author={Nabil Belacel and Guangze Wei and Yassine Bouslimani},
title={The k Closest Resemblance Classifier for Amazon Products Recommender System},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2020},
pages={873-880},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009155108730880},
isbn={978-989-758-395-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - The k Closest Resemblance Classifier for Amazon Products Recommender System
SN - 978-989-758-395-7
AU - Belacel N.
AU - Wei G.
AU - Bouslimani Y.
PY - 2020
SP - 873
EP - 880
DO - 10.5220/0009155108730880