Traditional recommendation systems aim at generating recommendations that are relevant to the user’s interest. Thus, they are called relevance-based recommendation systems (RBRSs). The major drawback of this approach is that user soon becomes very familiar with the recommendations and loses interest in reading and exploring them. In other words, relevance-based recommendations cannot help users to expand their interest and keep the recommendations exciting to them. Discovery-oriented recommendation systems (DORSs) aim to solve this problem by introducing discover utilities (DUs) such as novelty and diversity to improve the attractiveness of the recommendations to the user. In this thesis, we investigate techniques for improving the effectiveness of DORSs. Since novelty and divers...[ Read more ]

Traditional recommendation systems aim at generating recommendations that are relevant to the user’s interest. Thus, they are called relevance-based recommendation systems (RBRSs). The major drawback of this approach is that user soon becomes very familiar with the recommendations and loses interest in reading and exploring them. In other words, relevance-based recommendations cannot help users to expand their interest and keep the recommendations exciting to them. Discovery-oriented recommendation systems (DORSs) aim to solve this problem by introducing discover utilities (DUs) such as novelty and diversity to improve the attractiveness of the recommendations to the user. In this thesis, we investigate techniques for improving the effectiveness of DORSs. Since novelty and diversity are the most important and widely studied DUs, we focus on recommendation systems that aim to improve the novelty and diversity of the recommendations.

We study two important aspects of DORSs, namely, novelty and diversity of the recommendations. Existing DORSs generate recommendations that are optimized to balance between the accuracy and DUs of the recommendations to make the recommendations relevant and yet interesting to the user. However, they disregard an important fact that different users’ appetites for DUs are different. For example, a curious user can accept highly novel and diversified recommendations but a conservative user tends to respond only to recommendations she is familiar with. Thus, we propose a framework for curiosity-based recommendation systems (CBRSs) which can produce recommendations with an amount of DUs personalized to fit an individual user’s curiosity level. As a result, the recommendations are neither too surprising nor too boring for a user because the recommendations are customized to fit her unique curiosity. In order to model and quantify human curiosity, we adopt the curiosity arousing model (CAM) developed in psychology research and propose a probabilistic curiosity model (PCM) to model the psychological model computationally. Extensive experiments have been performed to evaluate the performance of CBRS against the baselines using a music dataset from last.fm. The results show that compared to the baselines CBRS not only provides more personalized recommendations that adapt to the user’s curiosity level but also improves the recommendation accuracy.

To improve the diversity of the recommendations, we propose a recommendation framework by the unification of two types of diversity, namely, intra-list and temporal diversity, of the recommendations. Traditional RBRSs recommend items which are very similar to the user’s interest. As a result, the recommended items are also very similar between each other, making the items in a recommendation list monotonous. We name this “intra-monotony problem” (IMP). Further, most existing recommendation systems make recommendations without considering what has been recommended before. Thus, they may make similar recommendations over and over again, making the recommended items across recommendation lists monotonous. We name this “temporal monotony problem” (TMP). To address these two problems, previous research has utilized intralist diversity (intraD) and temporal diversity (timeD) to improve, respectively, the diversity within a recommendation list and across recommendation lists. However, existing work studies these two diversity types separately. We propose an approach to unify the two diversity types into a single framework so that both intra-list and temporal diversity can be considered holistically. This is a challenging problem, since a high intra-list diversity does not guarantee a high temporal diversity, and vice versa. Rather than arbitrarily combining intraD and timeD, we propose a new diversity type called jointD and optimize it by formulating the problem as a constraint quadratic optimization problem. This approach allows both intraD and timeD to be jointly processed. We design a new performance metric called F-div to measure a recommendation system’s ability to improve the overall intraD and timeD. Experiment results show that optimizing jointD produces better F-div performance compared to optimize intraD or timeD alone.