Improvement to Recommendation system using Hybrid techniques

Title

Improvement to Recommendation system using Hybrid techniques

Creator

Ramesh R.; Vijayalakshmi S.

Description

Currently, recommendation systems are a common tool for providing individualized recommendations and item information to users. For personalization in the recommendation system, there are a variety of strategies that can be used. To improve system performance and offset the shortcomings of individual recommendation strategies, a hybrid recommender system integrates two or even more recommendation techniques. The demand to summarize all of the knowledge on actual methods and algorithms utilized in hybrid recommended systems necessitates the need for a systematic review in the domain. These materials will be employed to aid in the development of an auto-switching hybrid recommender system. In the content-based filtering technique, the algorithm is based on the contents of items and the collaborative filtering technique algorithm combines the relationship between user and item. Both of the approaches of recommendation system are suffers from some limitations, this is a big issue to predict better recommendations to the user. Hybrid systems are introduced to overcome the main limitations of both techniques. These systems are made with a combination of content-based and collaborative filtering techniques and have advantages of both techniques. With the use of hybrid systems, the quality of recommendations is improved. Hybrid recommendation systems use previous data of a user to find his/her interest and then they target the set of an adjacent user which is similar with that user and according to adjacent user recommend things to the user. Hybrid systems offer the items that share the common things that a user rated highly (Content-based filtering) and make suggestions by comparing the interest of a similar user (Collaborative filtering). 2022 IEEE.

Source

2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022, pp. 778-782.

Date

2022-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Collaborative Filtering method; Content-based filtering method; Hybrid Recommendation; Limitation; Movie Recommendation; User ratings

Coverage

Ramesh R., Christ (Deemed to Be University), Department of Data Science, Maharashtra, Pune, India; Vijayalakshmi S., Christ (Deemed to Be University), Department of Data Science, Maharashtra, Pune, India

Rights

Restricted Access

Relation

ISBN: 978-166543789-9

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICACITE53722.2022.9823879

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