A survey of multiple classifier systems as hybrid systems <s> Recommender systems <s> A passive, integrating electromagnetic radiation power dosimeter. A radiofrequency or microwave antenna is combined with a diode detector/rectifier, a squaring circuit, and a electrochemical storage cell to provide an apparatus for determining the average energy of electromagnetic radiation incident on a surface. After a particular period of irradiation, the dosimeter can be interrogated electrically or visually, depending on the type of electrochemical cell employed, to yield the desired information. The apparatus has a substantially linear response to the electromagnetic power density over a wide range of electromagnetic field, and all of the energy required to record the incident energy is supplied by the electromagnetic field. <s> BIB001 </s> A survey of multiple classifier systems as hybrid systems <s> Recommender systems <s> In electronic commerce applications, prospective buyers may be interested in receiving recommendations to assist with their purchasing decisions. Previous research has described two main models for automated recommender systems - collaborative filtering and the knowledge-based approach. In this paper, we present an architecture for designing a hybrid recommender system that combines these two approaches. We then discuss how such a recommender system can switch between the two methods, depending on the current support for providing good recommendations from the behaviour of other users, required for the collaborative filtering option. We also comment on how the overall design is useful to support recommendations for a variety of product areas and present some directions for future work. Overview One of the tasks in the application area of knowledgebased electronic markets is that of providing recommendations to potential shoppers. In an environment where there is a wide choice for the prospective buyers, an automated system which serves to present a more narrow selection for the buyer would be desirable. Recommender systems are systems which provide recommendations to potential buyers. Two widely used techniques for building recommender systems to date are collaborative filtering and knowledge-based approaches. Collaborative filtering is a real-time personalization technique that leverages similarities between people to make recommendations (Greening 1998). other words, a collaborative filtering recommender system assumes that human preferences are correlated; thus, it predicts preferences and makes recommendations to one user based on the preferences of a group of users. In contrast, a knowledge-based recommender system exploits its knowledge base of the product domain to generate recommendations to a user, by reasoning about what products meet the user’s requirements. In this paper, we analyze the advantages and shortcomings of both techniques and present an architecture for a hybrid recommender system that integrates the two approaches. Such a system will inherit all the strengths from a collaborative filtering recommender system, but will be able to avoid its weaknesses. Although there have been some proposals for designing systems which make use of both the knowledge-base approach and collaborative filtering (Burke 1999), collaborative filtering is used more in a post-processing stage, so that the knowledge-based design predominates. In this paper, we outline some specifications for changing between the collaborative filtering and the knowledge-based styles of recommendation, within a single system. This design strategy will be useful for electronic commerce applications where the number of buyers and the make-up of the community of buyers dictates whether collaborative filtering will be effective or not. Background In this section, we introduce the collaborative filtering and knowledge-based approaches in building recommender systems. We discuss the strengths and weaknesses of each approach as the motivation for the design of a hybrid architecture that combines the two approaches. <s> BIB002 </s> A survey of multiple classifier systems as hybrid systems <s> Recommender systems <s> Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering. <s> BIB003 </s> A survey of multiple classifier systems as hybrid systems <s> Recommender systems <s> We discuss learning a profile of user interests for recommending information sources such as Web pages or news articles. We describe the types of information available to determine whether to recommend a particular page to a particular user. This information includes the content of the page, the ratings of the user on other pages and the contents of these pages, the ratings given to that page by other users and the ratings of these other users on other pages and demographic information about users. We describe how each type of information may be used individually and then discuss an approach to combining recommendations from multiple sources. We illustrate each approach and the combined approach in the context of recommending restaurants. <s> BIB004 </s> A survey of multiple classifier systems as hybrid systems <s> Recommender systems <s> We present a framework for adaptive news access, based on machine learning techniques specifically designed for this task. First, we focus on the system's general functionality and system architecture. We then describe the interface and design of two deployed news agents that are part of the described architecture. While the first agent provides personalized news through a web-based interface, the second system is geared towards wireless information devices such as PDAs (personal digital assistants) and cell phones. Based on implicit and explicit user feedback, our agents use a machine learning algorithm to induce individual user models. Motivated by general shortcomings of other user modeling systems for Information Retrieval applications, as well as the specific requirements of news classification, we propose the induction of hybrid user models that consist of separate models for short-term and long-term interests. Furthermore, we illustrate how the described algorithm can be used to address an important issue that has thus far received little attention in the Information Retrieval community: a user's information need changes as a direct result of interaction with information. We empirically evaluate the system's performance based on data collected from regular system users. The goal of the evaluation is not only to understand the performance contributions of the algorithm's individual components, but also to assess the overall utility of the proposed user modeling techniques from a user perspective. Our results provide empirical evidence for the utility of the hybrid user model, and suggest that effective personalization can be achieved without requiring any extra effort from the user. <s> BIB005 </s> A survey of multiple classifier systems as hybrid systems <s> Recommender systems <s> The article presents a multidimensional (MD) approach to recommender systems that can provide recommendations based on additional contextual information besides the typical information on users and items used in most of the current recommender systems. This approach supports multiple dimensions, profiling information, and hierarchical aggregation of recommendations. The article also presents a multidimensional rating estimation method capable of selecting two-dimensional segments of ratings pertinent to the recommendation context and applying standard collaborative filtering or other traditional two-dimensional rating estimation techniques to these segments. A comparison of the multidimensional and two-dimensional rating estimation approaches is made, and the tradeoffs between the two are studied. Moreover, the article introduces a combined rating estimation method, which identifies the situations where the MD approach outperforms the standard two-dimensional approach and uses the MD approach in those situations and the standard two-dimensional approach elsewhere. Finally, the article presents a pilot empirical study of the combined approach, using a multidimensional movie recommender system that was developed for implementing this approach and testing its performance. <s> BIB006 </s> A survey of multiple classifier systems as hybrid systems <s> Recommender systems <s> Abstract A new approach to collaborative user modelling is presented in this paper. We have developed a framework that can be used for easy testing of different concepts. We have also introduced three different areas where collaborative modelling can be further improved. For the first phase of testing, we have created a hybrid system based on three different collaborative recommender techniques. Since this system implements multiple collaboration techniques, we decided to call this approach Combined Collaborative Recommender. Although each prediction technique can produce adequate results, we have proved that the combination of these techniques into a unified system provides a much more stable system. It should also be pointed out that these analyses were done using a very large dataset (more than 2 million ratings) providing reliable results. Results of these optimisations are presented along with pointers for further development. <s> BIB007 </s> A survey of multiple classifier systems as hybrid systems <s> Recommender systems <s> We analyze the application of ensemble learning to recommender systems on the Netflix Prize dataset. For our analysis we use a set of diverse state-of-the-art collaborative filtering (CF) algorithms, which include: SVD, Neighborhood Based Approaches, Restricted Boltzmann Machine, Asymmetric Factor Model and Global Effects. We show that linearly combining (blending) a set of CF algorithms increases the accuracy and outperforms any single CF algorithm. Furthermore, we show how to use ensemble methods for blending predictors in order to outperform a single blending algorithm. The dataset and the source code for the ensemble blending are available online. <s> BIB008 </s> A survey of multiple classifier systems as hybrid systems <s> Recommender systems <s> With the rapid growth of the World Wide Web (www), finding useful information from the Internet has become a critical issue. Web recommender systems help users make decisions in this complex information space where the volume of information available to them is huge. Recently, a number of Web page recommender systems have been developed to extract the user behavior from the user's navigational path and predict the next request as s/he visits Web pages. However, each of these systems has its own merits and limitations. In this paper, we investigate a hybrid recommender system, which combines the results of several recommender techniques based on Web usage mining. We conduct a detailed comparative evaluation of how different combined methods and different recommendation techniques affect the prediction accuracy of the hybrid recommender. We then discuss the results in terms of using a hybrid recommender system instead of a single recommender model. Our results suggest that the hybrid recommender system is better in predicting the next request of a Web user. <s> BIB009 </s> A survey of multiple classifier systems as hybrid systems <s> Recommender systems <s> Recommender systems could be used to help users in their access processes to relevant information. Hybrid recommender systems represent a promising solution for multiple applications. In this paper we propose a hybrid fuzzy linguistic recommender system to help the Technology Transfer Office staff in the dissemination of research resources interesting for the users. The system recommends users both specialized and complementary research resources and additionally, it discovers potential collaboration possibilities in order to form multidisciplinary working groups. Thus, this system becomes an application that can be used to help the Technology Transfer Office staff to selectively disseminate the research knowledge and to increase its information discovering properties and personalization capacities in an academic environment. <s> BIB010
Nowadays, recommender systems are the focus of intense research BIB006 . They try to help consumers to select the product that may be interesting for them based on their previous searches and transactions, but such systems are expanding beyond typical sales. They are used to predict which mobile telephone subscribers are in risk of switching to another provider, or to advice conference organizers about assigning papers to peer reviewers  . Burke BIB003 proposed hybrid recommender systems combining two or more recommendation techniques to improve performance avoiding the drawbacks of an individual recommender. Similar observations were confirmed by Balabanovic et al. BIB001 and Pazzani BIB004 who demonstrated that hybrid method recommentations improve collaborative and content-based approaches. There are several interesting works which apply the hybrid and combined approach to recommender systems. Jahrer and Töscher BIB008 demonstrated the advantage of ensemble learning applied to the combination of different collaborative filtering algorithms on the Netix Prize dataset. Porcel et al. BIB010 developed an hybrid fuzzy recommender system to help disseminate information about research resources in the field of interest of a user. Claypool et al.  performed a linear combination of the ratings obtained from individual recommender systems into one final recommendation, while Pazzani proposed to use a voting scheme BIB004 . Billsus and Pazzani BIB005 selected the best recommendation on the basis of a recommendation quality metric as the level of confidence while Tran and Cohen BIB002 preferred an individual which is the most consistent with the previous ratings of the user. Kunaver et al. BIB007 proposed Combined Collaborative Recommender based on three different collaborative recommender techniques. Goksedef and Gundoz-Oguducu BIB009 combined the results of several recommender techniques based on Web usage mining.