Boosting Memory-Based Collaborative Filtering Using Content-Metadata

QoS-based Web Service Recommendation using Popular-dependent Collaborative Filtering

Since, most of the organizations present their services electronically, the number of functionally-equivalent web services is increasing as well as the number of users that employ those web services. Consequently, plenty of information is generated by the users and the web services that lead to the users be in trouble in finding their appropriate web services. Therefore, it is required to provi...

Improving Memory-based Collaborative Filtering Using a Factor-based Approach

Collaborative Filtering (CF) systems generate recommendations for a user by aggregating item ratings of other like-minded users. The memory-based approach is a common technique used in CF. This approach first uses statistical methods such as Pearson’s Correlation Coefficient to measure user similarities based on their previous ratings on different items. Users will then be grouped into differen...

Improved Memory-based Collaborative Filtering Using Entropy-based Similarity Measures

Accuracy of predicting the user preference score is the most important element of collaborative filtering. This paper proposes novel similarity measures using difference score entropy of common rating items between two users. The proposed similarity measures can apply various weights according to the score difference, to evaluate the similarity. We implemented a recommender system using the pro...

Combining Collaborative and Content-Based Filtering Using Conceptual Graphs

Collaborative Filtering and Content-Based Filtering are techniques used in the design of Recommender Systems that support personalization. Information that is available about the user, along with information about the collection of users on the system, can be processed in a number of ways in order to extract useful recommendations. There have been several algorithms developed, some of which we ...

Combining Content-Based and Collaborative Filtering