Developing a Contextually Personalized Hybrid Recommender System
| Autor: | Aysun Bozanta, Birgul Kutlu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Information retrieval
Recall Article Subject Computer Networks and Communications Computer science 02 engineering and technology TK5101-6720 Recommender system Computer Science Applications Recommendation model Order (exchange) 020204 information systems 0202 electrical engineering electronic engineering information engineering Collaborative filtering Telecommunication Contextual information 020201 artificial intelligence & image processing Baseline (configuration management) |
| Zdroj: | Mobile Information Systems, Vol 2018 (2018) |
| ISSN: | 1574-017X |
| DOI: | 10.1155/2018/3258916 |
| Popis: | It is hard to choose places to go from an endless number of options for some specific circumstances. Recommender systems are supposed to help us deal with these issues and make decisions that are more appropriate. The aim of this study is to recommend new venues to users according to their preferences. For this purpose, a hybrid recommendation model is proposed to integrate user-based and item-based collaborative filtering, content-based filtering together with contextual information in order to get rid of the disadvantages of each approach. Besides that, in which specific circumstances the user will like a specific venue is predicted for each user-venue pair. Moreover, threshold values determining the user’s liking toward a venue are determined separately for each user. Results are evaluated with both offline experiments (precision, recall, F-1 score) and a user study. Both the experimental evaluation with a real-world dataset and a user study of the proposed system showed improvement upon the baseline approaches. |
| Databáze: | OpenAIRE |
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