From the observation that users reading news tend to not click outdated news, we propose notion of 'lifetime' with two hypotheses: (i) has a shorter lifetime, compared other types items such as movies or e-commerce products; (ii) only competes whose lifetimes have ended, and which an overlapping lifetime (i.e., limited competitions). By further developing characteristics then present novel appr...

Recommender system plays an increasingly important role in identifying the individual’s preference and accordingly makes a personalized recommendation. Matrix factorization is currently most popular model-based collaborative filtering (CF) method that achieves high recommendation accuracy. However, similarity computation hinders development of CF-based systems. Preference obtained only depends ...

In this paper, we suggest a novel recommender system where set of appropriate propositions is formed by measuring how user query features are close to space all possible propositions. The for e-traders selling commodities. A commodity has hierarchical-structure properties which mapped the respective numerical scales. scales normalized so that from potential customer and any proposition e-trader...

Recommender-system datasets are used for recommender-system offline evaluations, training machine-learning algorithms, and exploring user behavior. While there are many datasets for recommender systems in the domains of movies, books, and music, there are rather few datasets from research-paper recommender systems. In this paper, we introduce RARD, the Related-Article Recommendation Dataset, fr...

Recommender systems are becoming a salient part of many e-commerce websites. Much research has focused on advancing recommendation technologies to improve the accuracy of predictions, while behavioral aspects of using recommender systems are often overlooked. In this study, we explore how consumer preferences at the time of consumption are impacted by predictions generated by recommender system...

Mobile Application Recommender System Christoffer Davidsson With the amount of mobile applications available increasing rapidly, users have to put a lot of effort into finding applications of interest. The purpose of this thesis is to investigate how to aid users in the process of discovering new mobile applications by providing them with recommendations. A prototype system is then built as a p...

Memory-based collaborative filtering is the most popular approach to build recommender systems. Despite its success in many applications, it still suffers from several major limitations, including data sparsity. Sparse data affect the quality of the user similarity measurement and consequently the quality of the recommender system. In this paper, we propose a novel user similarity measure based...