Personalized ranking: Traditional ranking algorithms struggle to fully leverage multimodal information, and their limited model complexity fails to meet user demands for precise and personalized search.
Ultra-large-scale retrieval and ranking: Traditional discriminative cascaded ranking systems cannot meet the efficiency requirements for retrieval and ranking across hundred-billion-scale candidate pools.
Increasingly complex search needs: User search needs are growing increasingly complex. Traditional search frameworks struggle to accurately understand the semantics of long, complex, and ambiguous queries in multi-turn conversations, resulting in low search result satisfaction.
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Personalized ranking: Traditional ranking algorithms struggle to fully leverage multimodal information, and their limited model complexity fails to meet user demands for precise and personalized search.
Ultra-large-scale retrieval and ranking: Traditional discriminative cascaded ranking systems cannot meet the efficiency requirements for retrieval and ranking across hundred-billion-scale candidate pools.
Increasingly complex search needs: User search needs are growing increasingly complex. Traditional search frameworks struggle to accurately understand the semantics of long, complex, and ambiguous queries in multi-turn conversations, resulting in low search result satisfaction.
...
Personalized ranking: Traditional ranking algorithms struggle to fully leverage multimodal information, and their limited model complexity fails to meet user demands for precise and personalized search.
Ultra-large-scale retrieval and ranking: Traditional discriminative cascaded ranking systems cannot meet the efficiency requirements for retrieval and ranking across hundred-billion-scale candidate pools.
Increasingly complex search needs: User search needs are growing increasingly complex. Traditional search frameworks struggle to accurately understand the semantics of long, complex, and ambiguous queries in multi-turn conversations, resulting in low search result satisfaction.
...
Personalized ranking: Traditional ranking algorithms struggle to fully leverage multimodal information, and their limited model complexity fails to meet user demands for precise and personalized search.
Ultra-large-scale retrieval and ranking: Traditional discriminative cascaded ranking systems cannot meet the efficiency requirements for retrieval and ranking across hundred-billion-scale candidate pools.
Increasingly complex search needs: User search needs are growing increasingly complex. Traditional search frameworks struggle to accurately understand the semantics of long, complex, and ambiguous queries in multi-turn conversations, resulting in low search result satisfaction.
...
Build and maintain proactive talent pipelines for critical and recurring positions to support current and future manpower requirements.
Strengthen the Company's employer presence by participating in career fairs, campus recruitment, networking events, and other talent attraction initiatives.
Review the effectiveness of recruitment channels and sourcing strategies, and recommend continuous improvements to improve candidate quality, fill rate, and recruitment lead time.
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