In this paper we examine the effect of applying ensemble learning to the performance of collaborative filtering methods. We present several systematic approaches for generating an ensemble of collaborative filtering models based on a single collaborative filtering algorithm (single-model or homogeneous ensemble). We present an adaptation of several popular ensemble techniques in machine learnin...

New technologies impose to web-based groupware systems (which allow a group of user to work together through the system) to adapt their content to the constraints of such technologies and to the user’s mobile situation. We propose a context-based filtering process which aims at adapting the awareness information delivered to mobile users by these systems. Awareness information refers to the kno...

Collaborative filtering is one of the widely used technologies in the e-commerce recommender systems. It can predict the interests of a user based on the rating information of many other users. But the traditional collaborative filtering recommendation algorithm has the problems such as lower recommendation precision and weaker robustness. To solve these problems, in this paper we present a rob...

This study focuses on developing the multicriteria collaborative filtering algorithm for improving the prediction accuracy. The approaches applied were user-item multirating matrix decomposition, the measurement of user similarity using cosine formula and multidimensional distance, individual criteria weight calculation, and rating prediction for the overall criteria by a combination approach. ...

A plethora of collaborative filtering algorithms have been proposed in related literature. Due to the dynamic and changing parameters of the various application contexts, careful testing and parameterization has to be carried out before an algorithm is finally deployed in a real setting. This paper investigates how a previously proposed tool for simulated testing of collaborative filtering algo...

Although content is fundamental to our music listening preferences, the leading performance in music recommendation is achieved by collaborative-filtering-based methods which exploit the similarity patterns in user’s listening history rather than the audio content of songs. Meanwhile, collaborative filtering has the well-known “cold-start” problem, i.e., it is unable to work with new songs that...

Nowadays, item-based Collaborative Filtering (CF) has been widely used as an effective way to help people cope with information overload. It computes the item-item similarities/differentials and then selects the most similar items for prediction. A weakness of current typical itembased CF approaches is that all users have the same weight in computing the item relationships. In order to improve ...

Web service mining has become one of the promising areas of Service Oriented Architecture. Web service discovery methods include syntactic based system and semantic based system. Recommender system plays a vital role in recommending a product to customer, seldom used for web service recommendation. A variety of techniques have been proposed for performing recommendation namely, content-based, c...

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Automatic solutions which enable the selection of the best algorithms for a new problem are commonly found in the literature. One research area which has recently received considerable e‚orts is Collaborative Filtering. Existing work includes several approaches using Metalearning, which relate the characteristics of datasets with the performance of the algorithms. Œis work explores an alternati...