Recommender systems are intelligent systems which make suggestions about user items. Recommender system has become an important part of any entertainment or marketing website. As the recommender system has become so important it is a hot topic for any researcher. First paper on Recommender System was published in year 1998 since then more than 300 papers have been published in many of different...

Development of Web 2.0 enabled users to share information online, which results into an exponential growth of world wide web data. This leads to the so-called information overload problem. Recommender Systems (RS) are intelligent systems, helping on-line users to overcome information overload by providing customized recommendations on various items. In real world, people are willing to take adv...

Recommender system has been one of the main enabler of eCommerce personalization, and there have been many researches for improving the fitness of recommendation. However, the necessity of aggregated personal information has pointed out the privacy risk of recommender system. In this paper, we provide basic framework for analyzing the behavior of private information sharing in recommender syste...

Ant colony algorithms have become recently popular in solving many optimization problems because of their collaborative decentralized behavior that mimics the behavior of real ants when foraging for food. Recommender systems present an optimization problem in which they aim to accurately predict a user’s rating for an unseen item by trying to find similar users in the network. Trust-based recom...

Shilling Attack Prevention for Recommender Systems Using Social-based Clustering Tak Man Desmond Lee A Recommender System (RS) is a system that utilizes user and item information to predict the feeling of users towards unfamiliar items. Recommender Systems have become popular tools for online stores due to their usefulness in confidently recommending items to users. A popular algorithm for reco...

Recommender systems, notably collaborative and hybrid information filtering approaches, vitally depend on neighborhood formation, i.e., selecting small subsets of most relevant peers from which to receive personal product recommendations. However, common similarity-based neighborhood forming techniques imply various drawbacks, rendering the conception of decentralized recommender systems virtua...