Name
Abstract | ||
|---|---|---|
ABSTRACT Intent recommendation, as a new type of recommendation service, is to recommend a predicted query to a user in the search box when the user lands on the homepage of an application without any input. Such an intent recommendation service has been widely used in e-commerce applications, such as Taobao and Amazon. The most difficult part is to accurately predict user’s search intent, so as to improve user’s search experience and reduce tedious typing especially on mobile phones. Existing methods mainly rely on user’s historical search behaviors to estimate user’s current intent, but they do not make full use of the feedback information between the user and the intent recommendation system. Essentially, feedback information is the key factor for capturing dynamics of user search intents in real time. Therefore, we propose a feedback interactive neural network (FINN) to estimate user’s potential search intent more accurately, by making full use of the feedback interaction with the following three parts: 1) Both positive feedback (PF) and negative feedback (NF) information are collected simultaneously. PF includes user’s search intent information that the user is interested in, such as the query used and the title clicked. NF indicates user’s search intent information that the user is not interested in, such as the query recommended by the system but not clicked by the user. 2) A filter-attention (FAT) structure is proposed to filter out the noisy feedback and get more accurate positive and negative intentions of users. 3) A multi-task learning is designed to match the correlation between the user’s search intent and query candidates, which can learn and recommend query candidates from user interests and disinterests associated with each user. Finally, extensive experiments have been conducted by comparing with state-of-the-art methods, and it shows that our FINN method can achieve the best performance using the Taobao mobile application dataset. In addition, online experimental results also show that our method improves the CTR by 8% and attracts more than 7.98% of users than the baseline. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3442381.3450105 | International World Wide Web Conference |
Keywords | DocType | Citations |
Intent Recommendation, Query Suggestion, Query Understanding | Conference | 0 |
PageRank | References | Authors |
0.34 | 12 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yatao Yang | 1 | 2 | 3.07 |
| Biyu Ma | 2 | 34 | 1.58 |
| Jun Tan | 3 | 15 | 1.61 |
| Hongbo Deng | 4 | 861 | 41.00 |
| Haikuan Huang | 5 | 0 | 1.35 |
| Zibin Zheng | 6 | 3731 | 199.37 |