Using Quantitative Association Rules in Collaborative Filtering

Abstract. Recommender systems make information filtering for user by predicting user’s preference to items. Collaborative filtering is the most popular technique in implementing a recommender system. Association rule mining is a powerful data mining method to search for interesting relationships between items by finding the items frequently appeared together in a transaction database. In this paper, we apply quantitative association rules to mining the relationships between items, and then utilize the relationships between items to alleviate the data sparsity problem in the neighborhood-based algorithms. The proposed method considers not only similarities between users, but also similarities between items. The experimental results on two publicly available datasets show that our algorithm outperforms the conventional Pearson method and adjusted cosine method.

MARS: An Agent-Based Recommender System for the Semantic Web

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Multilayered Semantic Social Network Modelling by Ontology-Based User Profiles Clustering: Application to Collaborative Filtering

Abstract. We propose a multilayered semantic social network model that offers different views of common interests underlying a community of people. The applicability of the proposed model to a collaborative filtering system is empirically studied. Starting from a number of ontology-based user profiles and taking into account their common preferences, we automatically cluster the domain concept space. With the obtained semantic clusters, similarities among individuals are identified at multiple semantic preference layers, and emergent, layered social networks are defined, suitable to be used in collaborative environments and content recommenders.

Multi-criteria retrieval in cultural heritage recommendation systems

In recent years there has been a growing interest in mobile recommender systems for the tourism domain, because they can support users visiting new places, suggesting restaurants, hotels, attractions, or entire itineraries. The effectiveness of their suggestions mainly depends on the item retrieval process. How well is the system able to retrieve items that meet users' needs and preferences? In this paper we propose a multi-criteria collaborative approach, that offers a complete method for calculating users' similarities and rating predictions on items to be recommended. It is a purely multi-criteria approach, that uses Pearson's correlation coefficient to compute similarities among users. Experimental results evaluating the retrieval effectiveness of the proposed approach in a prototype mobile cultural heritage recommender system (that suggests visits to cultural locations in Apulia region) show a better retrieval precision than a standard collaborative approach based on the same metrics