Muaddib

Abstract

Web recommender systems are Web applications capable of generating useful suggestions for visitors of Internet sites. However, in the case of large user communities and in presence of a high number of Web sites, these tasks are computationally onerous, even more if the client software runs on devices with limited resources. Moreover, the quality of the recommendations strictly depends on how the recommendation algorithm takes into account the currently used device. Some approaches proposed in the literature provide multidimensional recommendations considering, besides items and users, also the exploited device. However, these systems do not efficiently perform, since they assign to either the client or the server the arduous cost of computing recommendations. In this article, we argue that a fully distributed organization is a suitable solution to improve the efficiency of multidimensional recommender systems. In order to address these issues, we propose a novel distributed architecture, called MUADDIB, where each user's device is provided with a device assistant that autonomously retrieves information about the user's behavior. Moreover, a single profiler, associated with the user, periodically collects information coming from the different user's device assistants to construct a global user's profile. In order to generate recommendations, a recommender precomputes data provided by the profilers. This way, the site manager has only the task of suitably presenting the content of the site, while the computation of the recommendations is assigned to the other distributed components. Some experiments conducted on real data and using some well-known metrics show that the system works more effectively and efficiently than other device-based distributed recommenders.

Understanding multi-channel research shoppers: an analysis of Internet and physical channels

“…Some EC leaders such as Amazon and Yahoo have also implemented the recommendation agents on their websites to better serve their customers (Dabholkar and Sheng 2012). Recently, some innovative multi-agent systems (e.g., MASHA, MUADDIB, and ARSEC) that emphasize collaboration and adaptability of users and devices in the distributed environment were developed to improve recommendation effectiveness (Rosaci and Sarné 2006;Rosaci et al 2009;Rosaci and Sarné 2012). Hence, online recommendation agents may influence the consumer attitudes toward using Internet for search or purchase.…”

A multi-agent recommender system for supporting device adaptivity in e-Commerce

“…However, in our approach, we propose the original idea of using clusters of similar customers to pre-compute collaborative filtering recommendations, off-line with respect to the activity of the seller agent that will use these recommendations when actually necessary. We have proposed to use clusters to extend the MASHA capabilities also in Rosaci et al (2009), in the context of a multi-agent architecture called MUADDIB. However, MUADDIB exploits a centralized mechanism to manage clusters, that is not suitable to be applied in an e-Commerce scenario.…”

“…The presence of more databases and computational entities make DRSs able to overcome typical CRSs limitations by offering a significant scalability, fault tolerance, privacy preservation and security safeguard. On the other hand, DRSs are characterized by an intrinsic difficulty in design and performances optimization (Ackerman et al 1999;Tanenbaum and Van Steen 2001;Canny 2002;Olson 2003;Zhong 2007) with a time and space complexity rapidly increasing with the number of involved entities to deal with (Jogalekar and Woodside 2000;Stormer 2007;Parikh and Sundaresan 2009;Rosaci et al 2009). Often DRSs adopt peer-to-peer (P2P) networks to easily exchange in a decentralized domain data locally stored on each peer and provide them with efficient, scalable and robust routing algorithms to reduce the task of locate specific resources (e.g., CAN (Ratnasamy and McCanne 1999), Chord (Stoica et al 2001), Pastry (Rowstron and Druschel 2001), and Tapestry (Zhao et al 2002)).…”

“…Each of these devices has its own physique and technological characteristics (as display or bandwidth capabilities) and usually is deeply different from its desktop counterpart; therefore, it is not suitable that providers deliver the same content to all device typologies. The necessity that recommender systems consider the exploited device in generating suggestions is becoming a key issue in the recommender systems field Sarnè 2006, 2010;De Meo et al 2003;Rosaci et al 2009). Indeed, if a user accesses a site with a traditional desktop PC, the site manager can propose its recommendations by using a presentation with a high structural complexity, supposing the user can benefit from such a presentation by means of his/her browser equipped with full functionalities.…”

A Television Recommender System Learning a User’s Time-Aware Watching Patterns Using Quadratic Programming

“…It is a kind of information filtering system that predicts users' ratings or preferences for items and provides users with items that they may be interested in. It is applied in a variety of domains such as movies, music, books, news, and any other contents in general [1,4,18,20,22,[25][26][27][28][29]. As a result, many recommendation algorithms have been developed for these domains in recent years.…”