Name
Abstract | ||
|---|---|---|
The quality of recommendations on social networks is a combination of the richness of the available information and the ability of algorithms and architectures to take advantage of this information in favor of the users. Recommendation algorithms have to address several problems, such as information sparsity, scalability of algorithms, concept drift etc. In this dynamic and complex environment, it is important to provide solutions that enrich information when it is necessary to fill the gaps and at the same time to scale solutions so that they can handle the ever increasing data sizes and flows. In this work, we extend our previous work on recommender systems for social networks that studied global influence and trust metrics and their applications. More specifically, we introduce a local influence model, which is relied on the formation of local user networks based on common interests and study the performance of the new model, both stand-alone and in combination with the global one. Results show a promising improvement on the similarity between a target user and the users recommended based on the users selected to influence the recommendations for that target user.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.23919/SPECTS.2019.8823538 | SummerSim |
Keywords | Field | DocType |
clustering, recommendation, social networks, trust | Recommender system,Social network,Computer science,Computer network,Concept drift,Artificial intelligence,Cluster analysis,Machine learning,Scalability | Conference |
ISBN | Citations | PageRank |
978-1-7281-3839-8 | 0 | 0.34 |
References | Authors | |
18 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nikolaos Mantas | 1 | 0 | 0.34 |
| Malamati D. Louta | 2 | 131 | 17.84 |
| Magdalini Eirinaki | 3 | 696 | 40.29 |
| Iraklis Varlamis | 4 | 503 | 52.08 |
| George T. Karetsos | 5 | 72 | 17.63 |