Hybrid Cluster based Collaborative Filtering using Firefly and Agglomerative Hierarchical Clustering

Recommendation Systems finds the user preferences based on the purchase history of an individual using data mining and machine learning techniques. To reduce the time taken for computation Recommendation systems generally use a pre-processing technique which in turn helps to increase high low performance and over comes over-fitting of data. In this paper, we propose a hybrid collaborative filtering algorithm using firefly and agglomerative hierarchical clustering technique with priority queue and Principle Component Analysis (PCA). We applied our hybrid algorithm on movielens dataset and used Pearson Correlation to obtain Top N recommendations. Experimental results show that the our algorithm delivers accurate and reliable recommendations showing high performance when compared with  existing algorithms

An improved model for trust-aware recommender systems based on multi-faceted trust

As customers enjoy the convenience of online shopping today, they face the problem of selecting from hundreds of thousands of products. Recommender systems, which make recommendations by matching products to customers based on the features of the products and the purchasing history of customers, are increasingly being incorporated into e-commerce websites. Collaborative filtering is a major approach to design algorithms for these systems. Much research has been directed toward enhancing the performance of recommender systems by considering various psychological and behavioural factors affecting the behaviour of users, e.g. trust and emotion. While e-commerce firms are keen to exploit information on social trust available on social networks to improve their services, conventional trust-aware collaborative filtering does not consider the multi-facets of social trust. In this research, we assume that a consumer tends to trust different people for recommendations on different types of product. For example, a user trusts a certain reviewer on popular items but may not place as much trust on the same reviewer on unpopular items. Furthermore, this thesis postulates that if we, as online shoppers, choose to establish trust on an individual while we ourselves are reviewing certain products, we value this individual’s opinions on these products and we most likely will value his/her opinions on similar products in future. Based on the above assumptions, this thesis proposes a new collaborative filtering algorithm for deriving multi-faceted trust based on trust establishment time. Experimental results based on historical data from Epinions show that the new algorithm can perform better in terms of accuracy when compared with conventional algorithms

Clustering Recommenders in Collaborative Filtering Using Explicit Trust Information

Part 2: Full PapersInternational audienceIn this work, we explore the benefits of combining clustering and social trust information for Recommender Systems. We demonstrate the performance advantages of traditional clustering algorithms like k-Means and we explore the use of new ones like Affinity Propagation (AP). Contrary to what has been used before, we investigate possible ways that social-oriented information like explicit trust could be exploited with AP for forming clusters of high quality. We conducted a series of evaluation tests using data from a real Recommender system Epinions.com from which we derived conclusions about the usefulness of trust information in forming clusters of Recommenders. Moreover, from our results we conclude that the potential advantages in using clustering can be enlarged by making use of the information that Social Networks can provide