[lian_multi-interest-aware_2021] "This paper proposes a Sequential User Matrix (SUM) to accurately and efficiently capture users' diverse interests. SUM models user behavior with a multi-channel network, with each channel representing a different aspect of the user's interests. User states in different channels are updated by an \emph{erase-and-add} paradigm with interest- and instance-level attention."

[cai2023reinforcing] "Reinforcing User Retention in a Billion Scale Short Video Recommender System. Recently, short video platforms have achieved rapid user growth by recommending interesting content to users. The objective of the recommendation is to optimize user retention, thereby driving the growth of DAU (Daily Active Users)."

[chen2021modeling] "Modeling Dynamic Attributes for Next Basket Recommendation. Traditional approaches to next item and next basket recommendation typically extract users' interests based on their past interactions and associated static contextual information (e.g. a user id or item category). However, extracted interests can be inaccurate and become obsolete."

[feng2021zero] "Zero Shot on the Cold-Start Problem: Model-Agnostic Interest Learning for Recommender Systems. User behavior has been validated to be effective in revealing personalized preferences for commercial recommendations. However, few user-item interactions can be collected for new users, which results in a null space for their interests, i.e.,"

Instructions: Rank the relevance between the given query and each given search result item. For each search result item, output one line with the format of only [source] and 1-5 score with 5 the highest relevent and 1 the lowest relevant. Do not output any other information.

Query: How to capture user interest?
Answer:  Provide exactly two different responses that are separated with 6 asterisk symbols: ******.
user