Abstract
The development of Web 2.0 and the rapid growth of available data have led to the development of systems, such as recommendation systems (RSs), that can handle the information overload. However, RS performance is severely limited by sparsity and cold-start problems. Thus, this paper aims to alleviate these problems. To realize this objective, a new model is proposed by integrating three sources of information: a user-item matrix, explicit and implicit relationships. The core strategy of this study is to use the multi-step resource allocation (MSRA) method to identify hidden relations in social information. First, explicit social information is used to compute the similarity between each pair of users. Second, for each non-friend pair of users, the MSRA method is applied to determine the probability of their relation. If the probability exceeds a threshold, a new relationship will be established. Then, all sources are incorporated into the Singular Value Decomposition (SVD) method to compute the missing prediction values. Furthermore, the stochastic gradient descent technique is applied to optimize the training process. Additionally, two real datasets, namely, Last.Fm and Ciao, are utilized to evaluate the proposed method. In terms of accuracy, the experiment results demonstrate that the proposed method outperforms eight state-of-the-art approaches: Heats, PMF, SVD, SR, EISR-JC, EISR-CN, EISR-PA and EISR-RAI.
Highlights
The amount of available data is growing rapidly and it is extremely complicated for users to find their preferences in this huge amount of data
RQ2: Does the incorporation of social information with rating feedback information into the Singular Value Decomposition (SVD) method improve the performance of the recommendation systems (RSs)?
A new model was proposed that exploits social information in the form of explicit and implicit relationships in addition to the user-item matrix
Summary
The amount of available data is growing rapidly and it is extremely complicated for users to find their preferences in this huge amount of data. Exploiting implicit social relationships for recommendation system enhancement user modelling, and classification learning [3]. Typical recommendation systems include collaborative filtering systems (CFs), content-based systems (CBs), and hybrid systems [4]. A CF learns a user-item matrix for recommending items. CFs are the most prevalent type of RSs and can be further subdivided into memory-based and model-based systems. A memory-based system computes the similarity of users/items to find the nearest n users/items for recommendation. The neighbour-model CF is a highly diffuse method of this type that depends on human ratings as the main feedback; the recommended items are identified by computing the similarities between users. A model-based system learns to recommend items to users by dividing the dataset into two parts (training and testing) and applying machine learning techniques
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