Name
Abstract | ||
|---|---|---|
Recommender systems have been regarded as gaining a more significant role with the emergence of the first research article on collaborative filtering (CF) in the mid-1990s. CF predicts the interests of an active user based on the opinions of users with similar interests. To extract information on the preference of users for a set of items and evaluate the performance of the recommender system’s techniques and algorithms, a critical analysis can be conducted. This study therefore employs a critical analysis on 131 articles in CF area from 36 journals published between the years 2010 and 2016. This analysis seems to be the exclusive survey which supports and motivates the community of researchers and practitioners. It is done by using the applications of users’ activities and intelligence computing and data mining techniques on CF recommendation systems. In addition, it provides a classification of the literature on academic database according to the benchmark recommendation databases, two users’ feedbacks (explicit and implicit feedbacks) which reflect their activities and categories of intelligence computing and data mining techniques. Eventually, this study provides a road map to guide future direction on recommender systems research and facilitates the accumulated and derived knowledge on the application of intelligence computing and data mining techniques in CF recommendation systems. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s00500-017-2918-7 | Soft Comput. |
Keywords | Field | DocType |
Collaborative recommendation systems, Public datasets, Survey, User feedback, Intelligence computing, Data mining techniques | Data science,Recommender system,Data mining,Collaborative filtering,Computer science,Road map,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
23 | 2 | 1432-7643 |
Citations | PageRank | References |
5 | 0.39 | 126 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| maryam khanian najafabadi | 1 | 33 | 2.31 |
| Azlinah Mohamed | 2 | 42 | 13.95 |
| Mohd Naz'ri Mahrin | 3 | 120 | 9.79 |
| NajafabadiMaryam Khanian | 4 | 14 | 0.87 |
| MohamedAzlinah Hj. | 5 | 5 | 0.39 |
| MahrinMohd Naz'ri | 6 | 31 | 1.83 |