Name
Title | ||
|---|---|---|
Recommendations For Mobile Apps Based On The Hits Algorithm Combined With Association Rules |
Abstract | ||
|---|---|---|
With the increasing popularity of intelligent devices, the mobile apps market has exploded. Due to a large number of candidate app services, it has become very difficult for users to choose the mobile apps that he/she wants to install. Therefore, it is crucial to improve users' experience and make personalized recommendations. In some cases, the traditional recommendation methods can be convenient, but they still have some shortcomings, resulting in inaccurate recommendations in general. To address this issue, this paper proposes a method for mobile app recommendations that are based on the Hyperlink-Induced Topic Search (HITS) algorithm combined with association rules. This method integrates the authority and hub scores into the candidate applications through the download and rating information, and it not only considers the importance of mobile apps in association rules but also takes the reliability factor of users into account. Experiments with the Huawei application market datasets show that the proposed method significantly improves the recommendation accuracies compared with the traditional methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2931756 | IEEE ACCESS |
Keywords | DocType | Volume |
Recommender systems, app recommendation, association rules, data mining | Journal | 7 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangliang Zhong | 1 | 0 | 0.68 |
| Yiwen Zhang | 2 | 0 | 1.35 |
| Dengcheng Yan | 3 | 0 | 0.34 |
| Qilin Wu | 4 | 2 | 2.05 |
| Yuan Ting Yan | 5 | 0 | 0.34 |
| Wei Li | 6 | 1 | 1.02 |