Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

Performance Comparison of Collaborative Filtering Prediction Methods on Recommendation System

Recommendation systems were introduced as the computer-based intelligent techniques to deal with the problem of information overload. Collaborative filtering is a simple recommendation algorithm that executes the similarity (neighborhoods) between items and then computes the missing data predictions. A serious limitation of collaborative filtering is the sparisity problem, referring to the situation where insufficient rating history is available for inferring reliable similarities. This research compares four prediction methods: Weighted Sum, Mean-Centering, Boosted Weighted Sum and Boosted Double Means Centering predictions. Boosting double means centering taken into account information of both users and items in order to overcome the potential decrease of accuracy due to sparsity when predicting the missing value. It tries to capture the user and item biases from the whole effects so as to enable the better concentrating on user-item interaction. Furthermore, ensemble learning will improve the performance collaborative filtering method because an ensemble of collaborative filtering models based on a single collaborative filtering algorithm considered the problem of sparsity, recommender error rate and sample weight update. Rating history in Book-Crossing dataset with 91% sparsity level is used to evaluate the missing rating predictions and the performance comparison of rating predictions on two traditional collaborative filtering and two boosting collaborative filtering frameworks. Experimental results shows that the proposed boosted double mean centering framework improve the prediction accuracy than the two traditional collaborative filtering and the other boosting prediction algorithm

Joint Deep Modeling of Users and Items Using Reviews for Recommendation

A large amount of information exists in reviews written by users. This source of information has been ignored by most of the current recommender systems while it can potentially alleviate the sparsity problem and improve the quality of recommendations. In this paper, we present a deep model to learn item properties and user behaviors jointly from review text. The proposed model, named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel neural networks coupled in the last layers. One of the networks focuses on learning user behaviors exploiting reviews written by the user, and the other one learns item properties from the reviews written for the item. A shared layer is introduced on the top to couple these two networks together. The shared layer enables latent factors learned for users and items to interact with each other in a manner similar to factorization machine techniques. Experimental results demonstrate that DeepCoNN significantly outperforms all baseline recommender systems on a variety of datasets.Comment: WSDM 201