Using Linked Data to Build Open, Collaborative Recommender Systems
نویسندگان
چکیده
While recommender systems can greatly enhance the user experience, the entry barriers in terms of data acquisition are very high, making it hard for new service providers to compete with existing recommendation services. This paper proposes to build open recommender systems which can utilise Linked Data to mitigate the new-user, new-item and sparsity problems of collaborative recommender systems. We describe how to aggregate data about object centred sociality from different sources and how to process it for collaborative recommendation. To demonstrate the validity of our approach, we augment the data from a closed collaborative music recommender system with Linked Data, and significantly improve its precision and recall.
منابع مشابه
A NOVEL FUZZY-BASED SIMILARITY MEASURE FOR COLLABORATIVE FILTERING TO ALLEVIATE THE SPARSITY PROBLEM
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...
متن کاملیک سامانه توصیهگر ترکیبی با استفاده از اعتماد و خوشهبندی دوجهته بهمنظور افزایش کارایی پالایشگروهی
In the present era, the amount of information grows exponentially. So, finding the required information among the mass of information has become a major challenge. The success of e-commerce systems and online business transactions depend greatly on the effective design of products recommender mechanism. Providing high quality recommendations is important for e-commerce systems to assist users i...
متن کاملA New WordNet Enriched Content-Collaborative Recommender System
The recommender systems are models that are to predict the potential interests of users among a number of items. These systems are widespread and they have many applications in real-world. These systems are generally based on one of two structural types: collaborative filtering and content filtering. There are some systems which are based on both of them. These systems are named hybrid recommen...
متن کاملA New Similarity Measure Based on Item Proximity and Closeness for Collaborative Filtering Recommendation
Recommender systems utilize information retrieval and machine learning techniques for filtering information and can predict whether a user would like an unseen item. User similarity measurement plays an important role in collaborative filtering based recommender systems. In order to improve accuracy of traditional user based collaborative filtering techniques under new user cold-start problem a...
متن کاملUsing a Data Mining Tool and FP-Growth Algorithm Application for Extraction of the Rules in two Different Dataset (TECHNICAL NOTE)
In this paper, we want to improve association rules in order to be used in recommenders. Recommender systems present a method to create the personalized offers. One of the most important types of recommender systems is the collaborative filtering that deals with data mining in user information and offering them the appropriate item. Among the data mining methods, finding frequent item sets and ...
متن کامل