Evaluation of Attribute-Aware Recommender System Algorithms on Data with Varying Characteristics

Abstract

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.

Tag-aware recommender systems by fusion of collaborative filtering algorithms

“…There have already been a reasonable amount of researches in using attributes as background knowledge in RS [2,3,4,8,7,14,12,17,16,20]. However, to the best of our knowledge, there hasn't been any research in considering tags with RS algorithms to predict items.…”

Attribute Aware Anonymous Recommender Systems

“…In contrast to this paper we will now describe models that make use of the product attributes, too. Attribute-based models have already been used to be useful on data with varying characteristics as described in [11].…”

Collaborative recommendation of e‐learning resources: an experimental investigation

“…2004). It is common to witness evaluation studies of proposed systems that engage usage data from totally different application domains, overlooking the particularities of their own domain (Tso & Schmidt‐Thieme 2006). This is why the importance of careful testing and parameterization of collaborative filtering systems, under conditions similar to the ones of their actual application, and before their actual deployment in real settings, has been outlined (Herlocker et al .…”

“…Nevertheless, related research identifies that a common drawback of such systems is that, often, they do not consider the needs of the actual online community that they aim to support (Herlocker et al 2004). It is common to witness evaluation studies of proposed systems that engage usage data from totally different application domains, overlooking the particularities of their own domain (Tso & Schmidt-Thieme 2006). This is why the importance of careful testing and parameterization of collaborative filtering systems, under conditions similar to the ones of their actual application, and before their actual deployment in real settings, has been outlined (Herlocker et al 2004;Manouselis & Costopoulou 2007a).…”