Traditional Similarity Models Research Articles

An efficient recommendation generation using relevant Jaccard similarity

In the literature, various collaborative filtering approaches have been developed to perform an efficient recommendation on top of reducing the search cost of the customers. The recommender system methods are concentrated on improving the accuracy and to achieve that goal they focused on formulating complex similarity approaches and neglect the computation time in their model. Furthermore, in order to compute the similarity metric, most of traditional similarity measures have only considered co-rated items and overlooked the total rating vector of the user or item. However, considering only co-rated items to measure similarity metrics in recommender system is an insignificant approach to identifying appropriate nearest neighbors in relatively sparse datasets. Therefore, in this research, two new simple but effective similarity models have been developed by considering all rating vectors of users to classify relevant neighborhoods and generate recommendations in a lower computation time. Moreover, MovieLens, a well-known dataset used in recommender system domain, is involved here to validate the performance of the proposed model. It seems that the proposed relevant Jaccard similarity perform more accurately and effectively to generate well recommendation than other traditional similarity models.

Time Aware and Data Sparsity Tolerant Web Service Recommendation Based on Improved Collaborative Filtering

With the incessant growth of web services on the Internet, how to design effective web service recommendation technologies based on Quality of Service (QoS) is becoming more and more important. Web service recommendation can relieve users from tough work on service selection and improve the efficiency of developing service-oriented applications. Neighborhood-based collaborative filtering has been widely used for web service recommendation, in which similarity measurement and QoS prediction are two key issues. However, traditional similarity models and QoS prediction methods rarely consider the influence of time information, which is an important factor affecting the QoS performance of web services. Furthermore, it is difficult for the existing similarity models to capture the actual relationships between users or services due to data sparsity. The two shortcomings seriously devalue the performance of neighborhood-based collaborative filtering. In this paper, the authors propose an improved time-aware collaborative filtering approach for high-quality web service recommendation. Our approach integrates time information into both similarity measurement and QoS prediction. Additionally, in order to alleviate the data sparsity problem, a hybrid personalized random walk algorithm is designed to infer indirect user similarities and service similarities. Finally, a series of experiments are provided to validate the effectiveness of our approach.