Abstract.One of the key challenges in large information systems such as online shops and digital libraries is to discover the relevant knowledge from the enormous volume of information. Recommender systems can be viewed as a way of reducing large information spaces and to personalize information access by providing recommendations for information items based on prior usage.Collaborative Filtering, the most commonly-used technique for this task, which applies the nearest-neighbor algorithm, does not make use of object attributes. Several so-called content-based and hybrid recommender systems have been proposed, that aim at improving the recommendation quality by incorporating attributes in a collaborative filtering model.In this paper, we will present an adapted as well as two novel hybrid techniques for recommending items. To evaluate the performances of our approaches, we have conducted empirical evaluations using a movie dataset. These algorithms have been compared with several collaborative filtering and non-hybrid approaches that do not consider attributes. Our experimental evaluations show that our novel hybrid algorithms outperform state-of-the-art algorithms.

Abstract. The growth of Internet commerce has provoked the use of Recommender Systems (RS). Adequate datasets of users and products have always been demanding to better evaluate RS algorithms. Yet, the amount of public data, especially data containing content information (attributes) is limited. In addition, the performance of RS is highly dependent on various characteristics of the datasets. Thus, few others have conducted studies on synthetically generated datasets to mimic the userproduct relationship. Evaluating algorithms based on only one or two datasets is often not sufficient. A more thorough analysis can be conducted by applying systematic changes to data, which cannot be done with real data. However, synthetic datasets that include attributes are rarely investigated. In this paper, we review synthetic datasets applied in RS and present our synthetic data generation methodology that considers attributes. Furthermore, we conduct empirical evaluations on existing hybrid recommendation algorithms and other state-of-the-art algorithms using these variable synthetic data and observe their behavior as the characteristic of data varies. In addition, we also introduce the use of entropy to control the randomness of the generated data.

Abstract. Collaborative Filtering (CF), the most commonly-used technique for recommender systems, does not make use of object attributes. Several hybrid recommender systems have been proposed, that aim at improving the recommendation quality by incorporating attributes in a CF model.In this paper, we conduct an empirical study of the sensitivity of attributes for several existing hybrid techniques using a movie dataset with an augmented movie attribute set. In addition, we propose two attribute selection measures to select informative attributes for attribute-aware CF filtering algorithms.

Abstract-As the amount of online shoppers grows rapidly, the need of recommender systems for e-commerce sites are demanding, especially when the number of users and products being offered online continues to increase dramatically. There have been many ongoing researches on recommender systems and in investigating recommendation algorithms that could optimize the recommendation quality. However, adequate and public datasets of users and products have always been demanding to better evaluate recommender system algorithms. Yet, the amount of public data, especially data containing adequate content information (attributes) is limited. When evaluating recommendation algorithms, it is important to observe the behavior of the algorithm as the characteristic of data varies. Synthetic data would allow the application of systematic changes on the data which cannot be done with real-life data. Although studies on synthetic data for the use of recommender systems have been investigated, artificial data with attributes information are rarely looked into. In this paper, we review public and synthetic data that are applied in the field of recommender systems. A synthetic data generation methodology that considers attributes will also be discussed. Furthermore, we present empirical evaluations on existing attributeaware recommendation algorithms and other state-of-theart algorithms using real-life dataset as well as variable synthetic data to observe their behavior as the characteristic of data varies. In particular, the informativeness of attributes is being further investigated with both real-life datasets with augmented attributes sets as well as synthetic datasets with attributes. We have shown that a reasonably good overview of the behavior of attribute-aware algorithms can be obtained by using synthetic data compared to results done with real-life datasets.

Abstract. Recommender systems are used by an increasing number of e-commerce websites to help the customers to find suitable products from a large database. One of the most popular techniques for recommender systems is collaborative filtering. Several collaborative filtering algorithms claim to be able to solve i) the new-item problem, when a new item is introduced to the system and only a few or no ratings have been provided; and ii) the user-bias problem, when it is not possible to distinguish two items, which possess the same historical ratings from users, but different contents. However, for most algorithms, evaluations are not satisfying due to the lack of suitable evaluation metrics and protocols, thus, a fair comparison of the algorithms is not possible.In this paper, we introduce new methods and metrics for evaluating the userbias and new-item problem for collaborative filtering algorithms which consider attributes. In addition, we conduct empirical analysis and compare the results of existing collaborative filtering algorithms for these two problems by using several public movie datasets on a common setting.