  Recommendation systems, which assist users in discovering their preferred
items among numerous options, have served billions of users across various
online platforms. Intuitively, users' interactions with items are highly driven
by their unchanging inherent intents (e.g., always preferring high-quality
items) and changing demand intents (e.g., wanting a T-shirt in summer but a
down jacket in winter). However, both types of intents are implicitly expressed
in recommendation scenario, posing challenges in leveraging them for accurate
intent-aware recommendations. Fortunately, in search scenario, often found
alongside recommendation on the same online platform, users express their
demand intents explicitly through their query words. Intuitively, in both
scenarios, a user shares the same inherent intent and the interactions may be
influenced by the same demand intent. It is therefore feasible to utilize the
interaction data from both scenarios to reinforce the dual intents for joint
intent-aware modeling. But the joint modeling should deal with two problems: 1)
accurately modeling users' implicit demand intents in recommendation; 2)
modeling the relation between the dual intents and the interactive items. To
address these problems, we propose a novel model named Unified Dual-Intents
Translation for joint modeling of Search and Recommendation (UDITSR). To
accurately simulate users' demand intents in recommendation, we utilize real
queries from search data as supervision information to guide its generation. To
explicitly model the relation among the triplet <inherent intent, demand
intent, interactive item>, we propose a dual-intent translation propagation
mechanism to learn the triplet in the same semantic space via embedding
translations. Extensive experiments demonstrate that UDITSR outperforms SOTA
baselines both in search and recommendation tasks.
