Name
Abstract | ||
|---|---|---|
Nowadays, many commercial products or services are promoted through the mobile device by using the mobile message (MS). How to provide proper and suitable MS to cope with user's preference is an important issue for business. To understand and infer the user message usage or user viewing behavior, ontology can be applied to conceptualize the user's preference and to construct the personal message profile. By incorporating the recommendation method, the mobile message can be recommended in terms of personalization. To increase the MS viewing ratio, this research proposed a Personalized Pair-Recommendation (PPR) method to provide not only one but related message for satisfying user's need. Our proposed PPR will analyze the mobile user's preference using the ontology of user's message preference. To illustrate the usefulness of our proposed method, we tested both Content-based (CB) method and Collaborative Filtering (CF) method, two major types of recommendation, to examine the precision, F1-measure, and the Successful Rate (SR). According to the results of the experiments, the proposed Personalized Pair-Recommendation (PPR) when integral with CF method can produce better outcome in terms of SR and F1-Measure. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICGEC.2011.21 | ICGEC |
Keywords | Field | DocType |
ontology,commercial services,ms viewing ratio,personalized pair-recommendation approach,ppr method,mobile device,mobile user,mobile message ontology,bundling,personal message profile,content-based method,adaptive resonance theory (art),message preference,pair-recommendation,mobile user preference,information filtering,mobile message,user viewing behavior,recommendation method,recommender systems,user message usage,cb method,cf method,user message preference,personalized pair-recommendation method,collaborative filtering method,satisfying user,ontologies (artificial intelligence),message passing,personalization,recommendation system (rs),mobile computing,f1-measure,content-based retrieval,commercial products,adaptive resonance theory,recommender system,filtering,strontium,business,collaborative filtering,mobile communication,collaboration,ontologies,satisfiability | Mobile computing,Computer science,Artificial intelligence,Message passing,Personalization,Ontology (information science),Recommender system,Collaborative filtering,Information retrieval,Mobile device,Multimedia,Machine learning,Mobile telephony | Conference |
ISBN | Citations | PageRank |
978-0-7695-4449-6 | 0 | 0.34 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li-Hua Li | 1 | 0 | 0.34 |
| Fu-Ming Lee | 2 | 10 | 2.31 |
| Tsung-Jen Pu | 3 | 1 | 0.72 |
| Chih-Wei Chen | 4 | 5 | 1.77 |