Name
Title | ||
|---|---|---|
Sequential Modeling of Hierarchical User Intention and Preference for Next-item Recommendation. |
Abstract | ||
|---|---|---|
The next-item recommendation has attracted great research interests with both static and dynamic users' preferences considered. Existing approaches typically utilize user-item binary relations, and assume a flat preference distribution over items for each user. However, this assumption neglects the hierarchical discrimination between user intentions and user preferences, causing the methods have limited capacity to depict intention-specific preference. In fact, a consumer's purchasing behavior involves a natural sequential process, i.e., he/she first has an intention to buy one type of items, followed by choosing a specific item according to his/her preference under this intention. To this end, we propose a novel key-array memory network (KA-MemNN), which takes both user intentions and preferences into account for next-item recommendation. Specifically, the user behavioral intention tendency is determined through key addressing. Further, each array outputs an intention-specific preference representation of a user. Then, the degree of user's behavioral intention tendency and intention-specific preference representation are combined to form a hierarchical representation of a user. This representation is further utilized to replace the static profile of users in traditional matrix factorization for the purposes of reasoning. The experimental results on real-world data demonstrate the advantages of our approach over state-of-the-art methods.
|
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3336191.3371840 | WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining
Houston
TX
USA
February, 2020 |
Keywords | Field | DocType |
Representation learning, memory networks, intention modeling | Information retrieval,Sequential modeling,Computer science | Conference |
ISBN | Citations | PageRank |
978-1-4503-6822-3 | 2 | 0.36 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nengjun Zhu | 1 | 15 | 4.02 |
| Jian Cao | 2 | 557 | 94.92 |
| Yanchi Liu | 3 | 698 | 45.70 |
| Yang Yang | 4 | 3 | 1.72 |
| Haochao Ying | 5 | 73 | 10.03 |
| Hui Xiong | 6 | 4958 | 290.62 |