In this paper we describe the KTIML team approach to RuleML 2015 Rule-based Recommender Systems for the Web of Data Challenge Track. The task is to estimate the top 5 movies for each user separately in a semantically enriched MovieLens 1M dataset. We have three results. Best is a domain specific method like "recommend for all users the same set of movies from Spielberg". Our contributions are d...

Normalized nonnegative models assign probability distributions to users and random variables to items; see [Stark, 2015]. Rating an item is regarded as sampling the random variable assigned to the item with respect to the distribution assigned to the user who rates the item. Models of that kind are highly expressive. For instance, using normalized nonnegative models we can understand users’ pre...

Motivated by applications in recommender systems, web search, social choice and crowdsourcing, we consider the problem of identifying the set of top K items from noisy pairwise comparisons. In our setting, we are non-actively given r pairwise comparisons between each pair of n items, where each comparison has noise constrained by a very general noise model called the strong stochastic transitiv...

Recommender systems apply data analysis techniques to the problem of helping users find the items they would like to purchase at E-Commerce sites by producing a predicted likeliness score or a list of top-N recommended items for a given user. We apply Clustering algorithms for finding nearest similar item. To finding nearest item for this we use C++ language. We apply improved K-mean algorithms...

Due to the data sparsity problem, social network information is often additionally used to improve the performance of recommender system. While most existing works exploit social information to reduce the rating prediction error, e.g., RMSE, a few had aimed to improve the top-k ranking prediction accuracy. This paper proposes a novel top-k oriented recommendation method, TRecSo, which incorpora...

Graphs are fundamental data structures and have been em-ployed for centuries to model real-world systems and phe-nomena. Random walk with restart (RWR) provides a goodproximity score between two nodes in a graph, and it hasbeen successfully used in many applications such as auto-matic image captioning, recommender systems, and link pre-diction. The goal of this work is t...

Graph-based recommendation models work well for top-N recommender systems due to their capability capture the potential relationships between entities. However, most of existing methods only construct a single global item graph shared by all users and regrettably ignore diverse tastes different user groups. Inspired success local tasks, this paper provides first attempt investigate multiple gra...

cold start is one of the main challenges in recommender systems. solving sparsechallenge of cold start users is hard. more cold start users and items are new. sine many general methods for recommender systems has over fittingon cold start users and items, so recommendation to new users and items is important and hard duty. in this work to overcome sparse problem, we present a new method for rec...