Web Mining and Social Networking: Techniques and ... - tud.ttu.ee

174 8 Web Mining and Recommendation Systemslearning is able to train the model (i.e. the item similarity matrix) before the real recommendationmaking, largely avoiding the computational difficulty and high time cost. Upon the learnedmodel, the further recommendation operation could be performed in a short time period, makingthe online recommendation feasible and operational. More importantly, such two-stagerecommendation scheme has become a well-adopted strategy for many recommender systemlater. Basically the computational complexity of such model-based CF systems requires anO(n 2 ) for a setting of n items.8.1.3 Performance EvaluationIn [218], comprehensive experiments are carried out to evaluate the proposed model-basedCF systems. In the following section, we briefly review the experimental setting and somerepresentative results.The dataset used in the experiments is from the MovieLens recommender system. Movie-Lens is a Web-based research recommender system built up in Fall 1997. Since Sagwar et al.published their work using the MovieLens dataset, later many researchers on recommendersystem research are continuing the use of this dataset for comparative studies. Even recentlysome latest recommendation work use it as a benchmark [222]. The dataset chosen for experimentscontains 943 users, 1682 movies and 100,000 ratings. For the chosen dataset, acertain percentage of whole dataset is separated for the model training purpose, while the restof dataset is left out for the test. In the experiments, different separation ratio values x areempirically investigated.Here we select several experimental results to present. Figure 8.3 depicts the impact oftrain/test separation ratio values and the neighborhood size on MAE using the two recommendationstrategies: item-item weighted sum and regression. Known from the figure, thebigger separation ratio values of train/test dataset always achieve the better recommendationperformance, indicating the larger training dataset is essential for an accurate recommendation.While for the selection of neighborhood size, the recommendation accuracy increaseswhen the neighborhood size is becoming bigger, and becomes stable after the neighborhoodsize reaches a certain value. The observed result implies that an appropriate neighborhoodsize achieves the best recommendation outcome, suggesting that the choosing a large numberof neighbors will only increase the computation cost but not benefit the recommendations.Similarly, in Fig.8.4, the recommendation comparisons of model-based and user-based CF al-Fig. 8.3. The impact of parameter x and neighborhood size [218]gorithms are carried out in terms of parameter x and neighborhood size. From the figure, it is