TY - GEN
T1 - Considering Interaction Sequence of Historical Items for Conversational Recommender System
AU - Tian, Xintao
AU - Hao, Yongjing
AU - Zhao, Pengpeng
AU - Wang, Deqing
AU - Liu, Yanchi
AU - Sheng, Victor S.
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Different from the traditional recommender systems with content-based and collaborative filtering, conversational recommender systems (CRS) can dynamically dialogue with users to capture fine-grained preferences. Although several efforts have been made for CRS, they neglect the importance of interaction sequences, which seek to capture the ‘context’ of users’ activities based on actions they have performed recently. Therefore, we propose a framework that considers interaction Sequence of historical items for Conversational Recommendation (SeqCR). Specifically, SeqCR first scores candidate items through the sequence which users interact with. Then it can generate the recommendation list and attributes to be asked based on the scores. We restrict candidate attributes to the ones with high-scoring (high-relevance) items, which effectively reduces the search space of attributes and leads to user preferences that can be hit more quickly and accurately. Finally, SeqCR utilizes the policy network to decide whether to recommend or ask. We conduct extensive experiments on two datasets from MovieLens 10M and Yelp in multi-round conversational recommendation scenarios. Empirical results demonstrate our SeqCR significantly outperforms the state-of-the-art methods.
AB - Different from the traditional recommender systems with content-based and collaborative filtering, conversational recommender systems (CRS) can dynamically dialogue with users to capture fine-grained preferences. Although several efforts have been made for CRS, they neglect the importance of interaction sequences, which seek to capture the ‘context’ of users’ activities based on actions they have performed recently. Therefore, we propose a framework that considers interaction Sequence of historical items for Conversational Recommendation (SeqCR). Specifically, SeqCR first scores candidate items through the sequence which users interact with. Then it can generate the recommendation list and attributes to be asked based on the scores. We restrict candidate attributes to the ones with high-scoring (high-relevance) items, which effectively reduces the search space of attributes and leads to user preferences that can be hit more quickly and accurately. Finally, SeqCR utilizes the policy network to decide whether to recommend or ask. We conduct extensive experiments on two datasets from MovieLens 10M and Yelp in multi-round conversational recommendation scenarios. Empirical results demonstrate our SeqCR significantly outperforms the state-of-the-art methods.
KW - Conversational recommendation
KW - Interactive recommendation
KW - Recommender system
UR - http://www.scopus.com/inward/record.url?scp=85104783979&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-73200-4_8
DO - 10.1007/978-3-030-73200-4_8
M3 - Conference contribution
AN - SCOPUS:85104783979
SN - 9783030731991
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 115
EP - 131
BT - Database Systems for Advanced Applications - 26th International Conference, DASFAA 2021, Proceedings
A2 - Jensen, Christian S.
A2 - Lim, Ee-Peng
A2 - Yang, De-Nian
A2 - Lee, Wang-Chien
A2 - Tseng, Vincent S.
A2 - Kalogeraki, Vana
A2 - Huang, Jen-Wei
A2 - Shen, Chih-Ya
PB - Springer Science and Business Media Deutschland GmbH
T2 - 26th International Conference on Database Systems for Advanced Applications, DASFAA 2021
Y2 - 11 April 2021 through 14 April 2021
ER -