Hybrid and Classical Models of Recommendation Systems- A Review

Recommendation Systems (RS) have evolved as a response to information overload caused by the growth in online content, increasing users' search time and information retrieval rate. By providing consumers with personalized suggestions based on their past preferences, likes, and dislikes, RS is used to find relevant content. Products, videos, photos, articles, news, and books are a few of the areas where these technologies are applied. Both attribute data, such as textual profiles and significant keywords, and user-item interactions such as ratings or purchasing activity are used by RS to analyze. This article provides an outline of the needs and difficulties of RS. To achieve greater recommendation capabilities in a variety of situations, this study attempts to explain the limits of the current generation of recommendation approaches and potential expansions with recommender systems. The results of the proposed methods have good improvement when compared with existing techniques.