Recommender system

Content-based recommenders have no inherent method for finding something unexpected. The system suggests items whose scores are high when matched against the user pro le, hence the user is going to be recommended items only similar to those already rated. This drawback is also called serendipity problem to highlight the tendency of the content-based systems to produce recommendations with a limited degree of novelty [4]. New user: Enough ratings have to be collected before a content-based recommender system

have been proposed for combining basic recommender system techniques to create a new hybrid system. The earlier survey of hybrids approaches [40] identified seven different types such as weighted, switching, mixed, feature combination, feature augmentation, cascade and meta-level [114]. A weighted hybrid recommender is one in which the score of a recommended item is computed from the results of all of the available recommendation techniques present in the system [40]. The score of different recommendation

7 CONCLUSION Most recommender systems work on single domains, i.e., they recommend items related to the same domain where users have expressed ratings. However, the integration of different domains into one recommender system could allow users to span over different types of items. For instance, in our research users that have watched movies could like to be recommended with on-demand movies and related books. This report focuses on cross-domain collaborative recommender systems, whose aim is to

Drawbacks of Collaborative and Content-Based Filtering Methods and the Advantages of Deep Belief Networks in Recommender Systems Sayali Borkar*, Girija Godbole*, Amruta Kulkarni* and Shruti Palaskar* *Computer Engineering Department, Pune Institute of Computer Technology, India Abstract—A large number of modern businesses are based on core idea of users consuming content in a physical or digital form, from a catalogue. The catalogue is available for browsing through a web site or mobile application

Recommender Systems are based on well-structured and incremental algorithms that have different strengths and weaknesses \cite{ricci2011introduction}. In general, the main existing RSs are concerned with improving accuracy with the premise of being useful to users. In this context, there is an increasing number of custom techniques that analyze the profile of the target user in order to better satisfy them \cite{bobadilla2013recommender}. In this sense, we conducted a broad study of the several metrics

perfectly fits a particular patient profile by representing a semantic recommendation system. The system has come up with HealtNet (HN) which is a core component of social network in which this recommender system is embedded. HealthNet’s goal is similar to PLM in terms of finding similar patients, sharing knowledge as well as looking for the experience. In addition to these features, HN has implanted recommender system which finds out the similarity between patients, recommends hospitals and patients

between certain things. From a Microsoft blog published in 2013 big data is “the process of applying serious computing power” (HowieT, 2013). Another article describes big data as data that “exceeds the processing capacity of conventional database systems” (Dumbill, 2012). Based on these definitions and many more alike, big data refers to or can be described as recorded information that exceeds capacity. As brief as this is, data can be recorded using many instruments and even through observation.

rated a movie that belongs to the comedy genre, then the system can learn to recommend other movies from this genre. Collaborative filtering: The simplest and original implementation of this approach recommends to the active user the items that other users with similar tastes liked in the past. The similarity in taste of two users is calculated based on the similarity in the rating history of the users. This is the reason why collaborative filtering is often referred as “people-to-people correlation

Various techniques have been used in content-based models. Such systems try to find regularities in the descriptions that can be used to distinguish highly rated items from others [97]. Content-based approaches are based on objective information about the items. This information is automatically extracted from various sources (e.g., Web pages) or manually introduced (e.g., product database). However, selecting one item or another is based mostly on subjective attributes of the item (e.g., a well-written

experiment was conducted with 487 subjects. Results indicate that subjects who consulted product recommendations selected recommended products twice as often as subjects who did not consult recommendations. The online recommendation source labeled “recommender system,”