Linked Open Data-Enabled Recommender Systems: ESWC 2014 Challenge on Book Recommendation
نویسندگان
چکیده
In this chapter we present a report of the ESWC 2014 Challenge on Linked Open Data-enabled Recommender Systems, which consisted of three tasks in the context of book recommendation: rating prediction in cold-start situations, top N recommendations from binary user feedback, and diversity in content-based recommendations. Participants were requested to address the tasks by means of recommendation appraoches that made use of Linked Open Data and semantic technologies. In the chapter we describe the challenge motivation, goals and tasks, summarize and compare the nine final participant recommendation approaches, and discuss their experimental results and lessons learned. Finally, we end with some conclusions and potential lines of future research.
منابع مشابه
Exploring Semantic Features for Producing Top-N Recommendation Lists from Binary User Feedback
In this paper, we report the experiments that we conducted for two of the tasks of the ESWC’14 Challenge on Linked Open Data (LOD)-enabled Recommender Systems. Task 2 and Task 3 dealt with the top-N recommendation problem from a binary user feedback dataset and results were evaluated on the accuracy and diversity respectively of the recommendations produced in a Top-N recommendation list for ea...
متن کاملA Hybrid Multi-strategy Recommender System Using Linked Open Data
In this paper, we discuss the development of a hybrid multistrategy book recommendation system using Linked Open Data. Our approach builds on training individual base recommenders and using global popularity scores as generic recommenders. The results of the individual recommenders are combined using stacking regression and rank aggregation. We show that this approach delivers very good results...
متن کاملHybrid Model Rating Prediction with Linked Open Data for Recommender Systems
We detail the solution of team uniandes1 to the ESWC 2014 Linked Open Data-enabled Recommender Systems Challenge Task 1 (rating prediction on a cold start situation). In these situations, there are few ratings per item and user and thus collaborative filtering techniques may not be suitable. In order to be able to use a content-based solution, linked-open data from DBPedia was used to obtain a ...
متن کاملLinked Open Data-enabled Strategies for Top-N Recommendations
The huge amount of interlinked information referring to different domains, provided by the Linked Open Data (LOD) initiative, could be e↵ectively exploited by recommender systems to deal with the cold-start and sparsity problems. In this paper we investigate the contribution of several features extracted from the Linked Open Data cloud to the accuracy of di↵erent recommendation algorithms. We f...
متن کاملLinked Data-based Conceptual Modelling for Recommendation: A FCA-Based Approach
In a recommendation task it is crucial to have an accurate contentbased description of the users and the items consumed by them. Linked Open Data (LOD) has been demonstrated as one of the best ways of obtaining this kind of content, given its huge amount of structured information. The main question is to know how useful the LOD information is in inferring user preferences and how to obtain it. ...
متن کامل