Abstract
In recommender systems, users’ preferences are expressed as ratings (either explicit or implicit) for items. In general, more ratings associated with users or items are elicited, more effective the recommendations are. However, almost all user rating datasets are sparse in the real-world applications. To acquire more ratings, the active learning based methods have been used to selectively choose the items (called interview items) to ask users for rating, inspired by that the usefulness of each rating may vary significantly, i.e., different ratings may bring a different amount of information about the user’s tastes. Nevertheless, existing active learning based methods, including both static methods and decision-tree based methods, encounter the following limitations. First, the interview item set is predefined in the static methods, and they do not consider the user’s responses when asking the next question in the interview process. Second, the interview item set in the decision tree based methods is very small (i.e., usually less than 50 items), which leads to that the interview items cannot fully reflect or capture the diverse user interests, and most items do not have the opportunity to obtain additional ratings. Moreover, these decision tree based methods tend to choose popular items as the interview items instead of items with sparse ratings (i.e., sparse items), resulting in “Harry Potter Effect” (http://bickson.blogspot.com.au/2012/09/harry-potter-effect-on-recommendations.html). To address these limitations, we propose a new active learning framework based on RBM (Restricted Boltzmann Machines) to add ratings for sparse recommendation in this paper. The superiority of this method is demonstrated on two publicly available real-life datasets.
| Original language | English |
|---|---|
| Title of host publication | Database Systems for Advanced Applications |
| Subtitle of host publication | 23rd International Conference, DASFAA 2018, Gold Coast, QLD, Australia, May 21-24, 2018, Proceedings, Part I |
| Editors | Jian Pei, Yannis Manolopoulos, Shazia Sadiq, Jianxin Li |
| Place of Publication | Cham Switzerland |
| Publisher | Springer |
| Pages | 100-115 |
| Number of pages | 16 |
| ISBN (Electronic) | 9783319914527 |
| ISBN (Print) | 9783319914510 |
| DOIs | |
| Publication status | Published - 2018 |
| Externally published | Yes |
| Event | Database Systems for Advanced Applications 2018 - Gold Coast, Australia Duration: 21 May 2018 → 24 May 2018 Conference number: 23rd https://www.springer.com/us/book/9783319914541 (Proceedings) |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Publisher | Springer |
| Volume | 10827 |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | Database Systems for Advanced Applications 2018 |
|---|---|
| Abbreviated title | DASFAA 2018 |
| Country | Australia |
| City | Gold Coast |
| Period | 21/05/18 → 24/05/18 |
| Internet address |
|
Cite this
}
Restricted Boltzmann Machine based active learning for sparse recommendation. / Wang, Weiqing; Yin, Hongzhi; Huang, Zi; Sun, Xiaoshuai; Hung, Nguyen Quoc Viet.
Database Systems for Advanced Applications : 23rd International Conference, DASFAA 2018, Gold Coast, QLD, Australia, May 21-24, 2018, Proceedings, Part I. ed. / Jian Pei; Yannis Manolopoulos; Shazia Sadiq; Jianxin Li. Cham Switzerland : Springer, 2018. p. 100-115 (Lecture Notes in Computer Science ; Vol. 10827 ).Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
TY - GEN
T1 - Restricted Boltzmann Machine based active learning for sparse recommendation
AU - Wang, Weiqing
AU - Yin, Hongzhi
AU - Huang, Zi
AU - Sun, Xiaoshuai
AU - Hung, Nguyen Quoc Viet
PY - 2018
Y1 - 2018
N2 - In recommender systems, users’ preferences are expressed as ratings (either explicit or implicit) for items. In general, more ratings associated with users or items are elicited, more effective the recommendations are. However, almost all user rating datasets are sparse in the real-world applications. To acquire more ratings, the active learning based methods have been used to selectively choose the items (called interview items) to ask users for rating, inspired by that the usefulness of each rating may vary significantly, i.e., different ratings may bring a different amount of information about the user’s tastes. Nevertheless, existing active learning based methods, including both static methods and decision-tree based methods, encounter the following limitations. First, the interview item set is predefined in the static methods, and they do not consider the user’s responses when asking the next question in the interview process. Second, the interview item set in the decision tree based methods is very small (i.e., usually less than 50 items), which leads to that the interview items cannot fully reflect or capture the diverse user interests, and most items do not have the opportunity to obtain additional ratings. Moreover, these decision tree based methods tend to choose popular items as the interview items instead of items with sparse ratings (i.e., sparse items), resulting in “Harry Potter Effect” (http://bickson.blogspot.com.au/2012/09/harry-potter-effect-on-recommendations.html). To address these limitations, we propose a new active learning framework based on RBM (Restricted Boltzmann Machines) to add ratings for sparse recommendation in this paper. The superiority of this method is demonstrated on two publicly available real-life datasets.
AB - In recommender systems, users’ preferences are expressed as ratings (either explicit or implicit) for items. In general, more ratings associated with users or items are elicited, more effective the recommendations are. However, almost all user rating datasets are sparse in the real-world applications. To acquire more ratings, the active learning based methods have been used to selectively choose the items (called interview items) to ask users for rating, inspired by that the usefulness of each rating may vary significantly, i.e., different ratings may bring a different amount of information about the user’s tastes. Nevertheless, existing active learning based methods, including both static methods and decision-tree based methods, encounter the following limitations. First, the interview item set is predefined in the static methods, and they do not consider the user’s responses when asking the next question in the interview process. Second, the interview item set in the decision tree based methods is very small (i.e., usually less than 50 items), which leads to that the interview items cannot fully reflect or capture the diverse user interests, and most items do not have the opportunity to obtain additional ratings. Moreover, these decision tree based methods tend to choose popular items as the interview items instead of items with sparse ratings (i.e., sparse items), resulting in “Harry Potter Effect” (http://bickson.blogspot.com.au/2012/09/harry-potter-effect-on-recommendations.html). To address these limitations, we propose a new active learning framework based on RBM (Restricted Boltzmann Machines) to add ratings for sparse recommendation in this paper. The superiority of this method is demonstrated on two publicly available real-life datasets.
UR - http://www.scopus.com/inward/record.url?scp=85048061080&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-91452-7_7
DO - 10.1007/978-3-319-91452-7_7
M3 - Conference Paper
SN - 9783319914510
T3 - Lecture Notes in Computer Science
SP - 100
EP - 115
BT - Database Systems for Advanced Applications
A2 - Pei, Jian
A2 - Manolopoulos, Yannis
A2 - Sadiq, Shazia
A2 - Li, Jianxin
PB - Springer
CY - Cham Switzerland
ER -