However, both collaborative and content-based methods have certain limitations. Moreover, in order to provide better recommendations and to be able to use recommender systems in arguably more complex types of applications, such as recommending vacations or certain types of financial services, most of the methods reviewed in this section would need significant extensions. Many real-life recommendation applications, including several business applications are arguably more complex than a movie recommender system, and would require taking more factors into the recommendation consideration. Therefore, the need to develop more advanced recommendation methods is even more pressing for such types of applications.
Based on how recommendations are made recommender systems can be categorized as:
? Content-based recommendations: the user is recommended items similar to the ones the user preferred in the past.
? Collaborative recommendations: the user is recommended items that people with similar tastes and preferences liked in the past.
? Hybrid approaches: these methods combine collaborative and content-based methods.

1. Content Based
2. by introducing some randomness. For example, the use of genetic algorithms Recommendations:
When the system can only recommend items that score highly against a user’s profile, the user is limited to being recommended items similar to those already rated. For example a person with no experience with Greek cuisine would never receive a recommendation for even the greatest Greek restaurant in town. This problem, which has also been studied in other domains, is often addressed has been proposed as a possible solution in the context of information filtering. Moreover the problem with over-specialization is not only that the content-based systems cannot recommend items that are different from anything the user has seen before. In certain cases, items should not be recommended if they are too similar to something the user has already seen, such as different news article describing the same event. Therefore, some content based recommender systems, such as Daily Learner filter out items not only if they are too different from user’s preferences but also if they are too similar to something the user has seen before. In short the diversity of recommendations is often a desirable feature in recommender systems. Ideally, the user should be presented with a range of options and not with a homogeneous set of alternatives.
Most importantly if the user is new he has to rate a sufficient number of items before a content-based recommender system can really understand user’s preferences and present the user with reliable recommendations. Therefore, a new user, having very few ratings, would not be able to get accurate recommendations