Semantic Grounding Strategies for Tagbased Recommender Systems

Recommender systems usually operate on similarities between recommended items or users. Tag based recommender systems utilize similarities on tags. The tags are however mostly free user entered phrases. Therefore, similarities computed without their semantic groundings might lead to less relevant recommendations. In this paper, we study a semantic grounding used for tag similarity calculus. We show a comprehensive analysis of semantic grounding given by 20 ontologies from different domains. The study besides other things reveals that currently available OWL ontologies are very narrow and the percentage of the similarity expansions is rather small. WordNet scores slightly better as it is broader but not much as it does not support several semantic relationships. Furthermore, the study reveals that even with such number of expansions, the recommendations change considerably.Comment: 13 pages, 5 figure

A Group Recommendation Model Using Diversification Techniques

In daily life groups are formed naturally, such as watching a movie with friends, or going out for dinner. In all these scenarios, using Recommendation Systems can be helpful by suggesting pieces of information (e.g. movies or restaurants) that satisfies all rather than a single member in the group. To do so, it is crucial to aggregate individual preferences of the group members aiming at satisfying all. Although there are consensus techniques to create the group profile, the recommendations still may be repetitive and overspecialized. This drawback sets precedent for adopting diversification techniques to group recommendations. In this paper, we propose a group recommendation model using diversification techniques that exploits different aggregation techniques over group preferences matrix. The experiments evaluate accuracy and diversity goals for the group recommendations. Results from the experiments point out that our approach achieved 1.8% of diversity increase and 3.8% of precision improvement over compared methods

A Linked Data browser with recommendations

It is becoming more common to publish data in a way that accords with the Linked Data principles. In an effort to improve the human exploitation of this data, we propose a Linked Data browser that is enhanced with recommendation functionality. Based on a user profile, also represented as Linked Data, we propose a technique that we call LDRec that chooses in a personalized way which of the resources that lie within a certain neighbourhood in a Linked Data graph to recommend to the user. The recommendation technique, which is novel, is inspired by a collective classifier known as the Iterative Classification Algorithm. We evaluate LDRec using both an off-line experiment and a user trial. In the off-line experiment, we obtain higher hit rates than we obtain using a simpler classifier. In the user trial, comparing against the same simpler classifier, participants report significantly higher levels of overall satisfaction for LDRec