Recommendation System: A Literature Survey
Mohan Kubendrian1, Nishal Pradhan2

1Dr.Mohan Kubendiran, Associate Professor, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
2Nishal Pradhan, PG student, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.

Manuscript received on 01 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 2153-2161 | Volume-8 Issue-7, May 2019 | Retrieval Number: G6205058719/19©BEIESP
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Abstract: Recommendation systems aim at identifying the best products and contents that suits the preference of a user. It has become increasingly popular in a number of areas, like recommending books, news articles, movies, music, commercial products, restaurants, web pages, and many more. Retail companies and e-commerce sites take full advantage of recommender systems in order to boost their profit margins by boosting sales or rather leverage the data that are provided to them. To improve performance and accuracy for a more specialized recommendation for each customer, there has been a lot of research on developing hybrid recommendation systems instead of improving collaborative or content-based methods alone. Hybrid systems club together both content-based and collaborative based methods. LightFM, a hybrid model, has been proven to be the most effective when it comes to recommendation systems. This makes it interesting to study the effectiveness of LightFM compared to other existing models. In this study, we provide a literature survey of the existing recommendation systems, with our focus based on LightFM which is used for implicit feedback and user-item cold start problems.
Keyword: Collaborative Filtering, Content-Based Filtering, Explicit Feedback, Hybrid Filtering, Implicit Feedback, Light FM.
Scope of the Article: Signal Control System & Processing.